Find Your Dream Data Science Job in India

Job Title: Data Science Trainer

Organization: SkillCircle

Location: CP , Pitampura

Employment Type: Full-time

Job Summary:

SkillCircle is looking for a passionate and knowledgeable Data Science Trainer to

mentor and guide students through their journey into the world of data science.

The ideal candidate should have a strong foundation in Python, machine learning,

data analytics, and the ability to simplify complex concepts for learners at various

levels.

Required Skills:

Proficiency in Python, Pandas, NumPy, Scikit-learn, Matplotlib/Seaborn,

Tableau and Power BI.

Knowledge of Machine Learning algorithms and their real-world

applications.

Understanding of statistics, data preprocessing, and EDA.

Excellent communication and presentation skills.

Ability to engage and inspire students from non-technical backgrounds.

Eligibility Criteria:

Bachelor's degree in Computer Science, Data Science, Engineering, or a

related field.

2-5 years of experience in teaching/training or industry experience in data

science roles.

Prior experience as a trainer or mentor is a plus.

Certifications in Data Science or Machine Learning (preferred but not

mandatory).

Perks and Benefits:

Flexible working hours

Exposure to a large network of learners and tech professionals.

Opportunity to work on live projects and build personal brand through

content/webinars.

Job Title: Data Science Trainer

Company: WsCube Tech

Location: Jaipur, Rajasthan

Role Type: Full-Time

Experience Required: 2+ Years (Industry or Training)

About WsCube Tech:

WsCube Tech is a Vernacular Upskilling EdTech company dedicated to creating job-ready professionals for the global workforce. Our mission is to bridge skill gaps with tech-powered career acceleration programs and impact over 100 million career aspirants in the next five years.

You can learn more about us via our website at

and YouTube channel at

About the Role:

WsCube Tech is looking for an experienced Data Science Trainer who can deliver high-quality training on Data Analytics, Machine Learning, and industry tools. The trainer should be comfortable teaching both theory and practical concepts and guiding learners toward industry readiness.

Key Responsibilities:

• Deliver training sessions on Python, SQL, Machine Learning, Statistics, and Power BI.
• Conduct classroom training, doubt-solving, and one-on-one learner mentorship.
• Prepare and update course material, assignments, assessments, and real-world projects.
• Evaluate learner performance and share structured feedback.
• Guide learners on project building, interview preparation, and industry expectations.
• Work closely with the academic team for curriculum enhancement and training quality.
• Maintain an engaging and supportive learning environment.
• Stay updated with industry trends and integrate practical examples in sessions.

Technical Skills Required:

• Strong hands-on experience with Python (NumPy, Pandas, Matplotlib, Seaborn).
• Solid understanding of Machine Learning algorithms and model building.
• Practical knowledge of SQL and relational databases.
• Experience with Power BI or Tableau for data visualization.
• Strong understanding of Statistics, EDA, Hypothesis Testing, and Data Wrangling.
• Knowledge of Deep Learning, NLP, or cloud platforms is an added advantage.

Role Name: Data Scientist

Total IT Experience: 6+ ; Relevant Datascience(Python) Experience: 4+

Location: PAN India

Job Description:

• Expert level developer with a very strong Python (Numpy, Pandas, siki-learn and other data science libraries)
• Expert level developer with SQL knowledge (Expert level Data manipulation, wrangling, transformation)
• Expertise in writing clean and production level AI/ML code using Machine learning and Deep Learning algorithms
• Experience in deploying Vertex AI or similar platforms
• Business acumen to understand the business requirements
• Should have expert in hands on understanding of Machine learning algorithms like Linear, logistic regression, Decision Tree, Random Forest , XGboost , K-means Clustering

Technical Stack: Data Science, Machine Learning algorithms, Statistics, SQL, Python, GCP MLops, deploying in Vertex AI or similar platforms

Technical Content Developer

About NxtWave

NxtWave was founded by Rahul Attuluri (ex-Amazon, IIIT Hyderabad), Sashank Reddy (IIT Bombay), and Anupam Pedarla (IIT Kharagpur). In February 2023, NxtWave raised 275 crore, led by Greater Pacific Capital, a leading international private equity firm. The startup is also backed by Orios Ventures, Better Capital, and marquee angels, including the founders of some of India's unicorns.

NxtWave is one of India's fastest-growing Ed-Tech startups, revolutionising the 21st-century job market by transforming youth into highly skilled tech professionals irrespective of their educational background with its CCBP 4.0 programs.

As an official partner for NSDC under the Ministry of Skill Development & Entrepreneurship, Government of India, and recognised by NASSCOM, the Ministry of Commerce and Industry, Government of India, and Startup India, NxtWave has earned a reputation for excellence. The startup has received accolades such as 'The Greatest Brand in Education' in a research-based listing by URS Media, a leading international media house. NxtWave has also been honoured with the "Startup Spotlight Award 2023" by T-Hub on its 8th Foundation Day.

Recently, NxtWave's founders, Anupam Pedarla and Sashank Gujjula, were honoured in the 2024 Forbes India 30 Under 30 for their exceptional contribution to transforming the tech education landscape in India.

The edtech brand eliminates the entry barrier to learning tech skills by offering vernacular content and interactive learning. Learning in one's mother tongue helps achieve higher comprehension, deeper attention, longer retention, and greater outcomes.

Presently, NxtWave has paid subscribers from 647+ districts across India. In just 2.5 years, CCBP 4.0 learners have been hired by 2000+ companies, including Google, Amazon, Nvidia, Goldman Sachs, Oracle, Deloitte, and more.

Know more about NxtWave:

• Our LinkedIn page: Link
• Next wave of opportunities with 1700+ companies: Link
• 33M funding news: Link
• YouTube Channel: Link
• Impact Stories on LinkedIn: Link

Read more about us in the news: Economic Times CNBC Yourstory VCCircle

Job Description:

At NxtWave, as a TCD, you will be heading your technical pod and will be responsible for creating and refining industry-relevant content for students. This role involves developing engaging content and materials that enhance learning experiences, ensuring industry relevance, and driving continuous improvement. You'll collaborate with teams internally and SMEs externally to manage technical content tracks, analyse industry trends, and deliver high-quality outcomes that improve learners' skills and employability.

Skills Required

• Minimum 1-4 years of experience in content development.
• Strong understanding of neural networks, CNNs, RNNs, LSTMs, GANs, transformers, and other advanced architectures.
• Expertise in Python and popular Deep Learning frameworks like TensorFlow, PyTorch, and Keras.
• Hands-on experience building, training, and deploying Deep Learning Models.
• Experience working with real-world datasets for image recognition, NLP, time series forecasting, and other domains.
• Knowledge of pre-trained models and transfer learning techniques.

Responsibilities

• Lead a team of Associate technical content developers to ensure timely and high-quality content creation.
• Develop high-quality, industry-relevant content on Deep Learning concepts, including neural networks, CNNs, RNNs, GANs, transformers, and other advanced topics.
• Incorporate hands-on projects and real-world datasets to provide practical learning experiences.
• Understanding of how to develop content to meet various learner needs and styles.
• Collaborate with Subject Matter Experts (SMEs) to ensure technical accuracy and relevance of the content.
• Design curriculum outlines, learning objectives, and roadmaps for comprehensive technical programs.
• Stay updated with the latest industry trends to incorporate cutting-edge topics and technologies into the content.
• Review and revise content regularly based on learner feedback and emerging educational strategies.
• Act as a bridge between content development and product teams, driving feature development and design improvements to support immersive and practical learning experiences.
• Monitor and report on the progress of content development to ensure alignment with organisational goals.

• Location: NxtWave Office Spaces in Hyderabad
• Job Type: Full-Time
• Working days: 5 days a week

Minimum years of Experience - 15 years

Collinson

Collinson is the global, privately-owned company dedicated to helping the world to travel with ease

and confidence.

We use our expertise and products to craft customer experiences which enable some of the world's

best known brands to acquire, engage and retain the most demanding and choice-rich customers. In

particular, our unique expertise and insight into high earning, frequent travellers allows us to create

products and solutions for our clients that inspire greater customer engagement to drive more

profitable relationships, enrich their travel experiences, protect what matters and assist in in times of

need.

While specialising in Financial Services, Travel and Retail, we also support clients in multiple sectors.

We have worked with over 90 airlines, 20 hotel groups and more than 600 financial institutions and

banks, with clients including Accor Hotels, Air France KLM, Amex, British Airways,

Cathay Pacific, Diners Club, Mandarin Oriental, Mastercard, Radisson Hotel Group, Sephora, Visa

and Vhi.

We take our 30 years' experience working with these kinds of household names in over 170

countries, and help our clients to deliver the smarter experiences it takes to differentiate their

propositions, and help them win deeper devotion with their customers.

Collinson is a privately-owned entrepreneurial business with 2,000 passionate people working in 20

locations worldwide. Our solutions include Priority Pass, the world's best known airport experiences

programme, while we are also the trusted partner behind many of the leading financial services,

airline and hotel brand's reward programmes and loyalty initiatives.

Purpose of the job

The Head of Data Science is a senior position responsible for shaping, operationalizing, and

advancing the organization's data science strategy. This role focuses on creating high-value, data

driven insights and solutions that drive innovation, improve decision-making, and deliver measurable

business outcomes. You will act as the bridge between business strategy and advanced analytics

execution, ensuring that data science becomes a central enabler of enterprise growth, customer

engagement, and operational excellence.

By leading a portfolio of data science initiatives, you will collaborate with senior stakeholders across

business units, translating high-level business objectives into scalable, production-grade data science

solutions. This role demands deep technical expertise, strong leadership skills, and the ability to

deliver solutions that strengthen the organization's competitive advantage.

The ideal candidate is a visionary leader who understands both the technical and business aspects of

data science. You will partner with teams across the company to define strategies, deliver impactful

projects, and embed data science into core business operations.

Key Responsibilities

• Establish and lead a high-performing Data Science function, fostering an environment of

innovation, experimentation, and analytical excellence.

• Own the organizational data science strategy, including defining and delivering the

intelligent data products vision and roadmap.

• Drive the full lifecycle of data science projects, from problem framing and data exploration

to model development, validation, deployment, and scaling.

• Partner with senior leaders across platform, product, and business units to refine and

execute the organizational data science strategy, ensuring alignment with business goals.

• Collaborate with stakeholders to identify high-impact use cases and prioritize initiatives

that deliver measurable business value.

• Deliver advanced analytics and machine learning solutions addressing challenges such

as customer personalisation, operational optimisation, fraud detection, predictive

forecasting and more.

• Manage and mentor a team of data scientists, developing their technical and business

skills while aligning their efforts with organizational priorities.

• Oversee the integration of data science solutions into existing systems, ensuring

robustness, scalability, and long-term performance.

• Promote innovation by exploring new data science applications beyond current

ecosystem limitations.

• Champion best practices in data governance, ethical data standards, and regulatory

compliance.

• Stay ahead of emerging trends in data science, analytics, and AI to introduce innovations

that reinforce the organization's market leadership.

• Lead the adoption of robust experimentation and causal inference methods (A/B testing,

uplift testing) to embed an evidence-based culture across the business.

• Partner closely with Data Engineering and Platform teams to ensure high-quality

pipelines, reliable feature stores, and scalable production deployments.

• Drive the operationalization of predictive models in areas such as demand forecasting,

customer lifetime value, churn prediction, and fraud detection.

• Champion the use of advanced analytics for decision support, ensuring insights are

actionable, timely, and embedded into business processes.

• Ensure all initiatives deliver measurable commercial ROI, linking data science outcomes

to customer loyalty, revenue growth, and cost optimization.

Impact Areas

• Enable data-driven products and solutions that enhance customer acquisition, loyalty,

and retention.

• Operational Efficiency: Drive cost optimization and decision-making through advanced

analytics and predictive modelling.

• Customer Experience: Deliver personalized, real-time services powered by data insights

and advanced analytics.

• Experimentation Culture: Build a company-wide discipline of testing, learning, and scaling

insights.

• Commercial Value Realization: Ensure every data science initiative is measured against

its impact on revenue, retention, and efficiency.

• Scalable Deployment: Deliver models that are production-ready, resilient, and aligned

with enterprise-grade standards

Knowledge, skills, and experience required

• Proven track record in leading data science projects with measurable business outcomes

in a commercial setting.

• Expertise in statistical modelling, machine learning, predictive analytics, and data-driven

decision-making.

• Strong experience with data science tools, frameworks, and cloud platforms.

• Proficiency in designing and managing end-to-end data science workflows, including data

preparation, model training, deployment, and monitoring.

• Leadership experience managing teams of data scientists or analysts in fast-paced,

innovation-driven environments.

• Ability to engage with C-suite stakeholders, translate business needs into analytical

solutions, and communicate results effectively.

• Deep understanding of data ethics, privacy, and governance frameworks.

• Proven expertise in applied machine learning, predictive modelling, and statistical

analysis within large, complex organizations.

• Strong track record of deploying and maintaining models in production, with emphasis on

scalability, monitoring, and lifecycle management.

• Experience designing and leading experimentation frameworks to validate business

impact.

• Proficiency in working with modern data architectures (e.g., lakehouse, feature stores,

streaming systems) to enable real-time analytics and ML.

• Commercial acumen with the ability to translate analytical outputs into tangible business

outcomes.

Hiring Principal Data Scientist (IC Leadership Role)

We are looking for a Principal Data Scientist to drive high-impact AI/ML initiatives, shape data strategy, and solve complex business problems at scale.

Location: Mumbai (Work from Office)

Experience: 10+ Years

Key Responsibilities

• Lead advanced ML, statistical & Agentic AI solutions

• Own end-to-end model lifecycle (design deployment)

• Drive experimentation, causal inference & decision science

• Partner with business & product leaders on data strategy

• Mentor teams & define best practices and standards

Must Have

• Strong expertise in ML, Statistics & Python/SQL

• Experience with production-grade ML systems

• Proven track record of solving complex, high-impact problems

Good to Have

• Cloud (AWS/GCP/Azure), MLOps, Spark/BigQuery/Snowflake

• Domain expertise in NLP, forecasting, personalization, risk

Ideal for senior ICs who want to drive technical leadership without people management.

Interested or referrals? Share CV at:

Dun & Bradstreet Technology and Corporate Services India LLP is looking for candidates to support the Data Science team in Trade and Credit Risk Analytics. The candidate needs to work closely with the team based in India and across a range of Analytics leaders who are located globally to fulfill delivery on a timely basis

Key Responsibilities:

• Work on development of B2B Risk solutions which includes Standard and custom solutions catering to various clients including fortune 500 companies
• Work with internal / external D&B clients and stakeholders; Participate in all aspects of a modelling engagement, including design, development, validation, calibration, documentation, approval, implementation, monitoring, and reporting
• Ability to applying LLMs, and prompt engineering to analyze large-scale, unstructured and structured B2B datasets (e.g., Company News, Corporate Annual Reports) for credit risk, fraud detection, and compliance.
• Design, develop and test new risk signals to effectively identify risk patterns from structured and Unstructured data
• Serve as a Subject Matter Expert on risk models within the Analytics team and with business users; consult with the business, as appropriate, on predictive modelling solutions
• Develop AI Agents for business risk monitoring, deploying autonomous agents. These agents utilize Machine Learning (ML) and Natural Language Processing (NLP) to detect risk triggers, anomalies in real-time, shifting risk management from reactive reporting to predictive, actionable insights
• Ability to manage multiple assignments, many of which with challenging timelines
• Ability to work independently, as well as collaborate effectively in a team environment
• Partner with internal D&B team to develop new business solutions in risk analytics

Key Skills:

What we are looking for:

• Master's degree or higher with concentration in a quantitative discipline such as (Math/Stat, Economics, Computer Science, Finance, Operations Research, etc.) with 5 - 8 years of experience in Data Science.
• Proven experience on design and development of Risk models and frameworks
• Experience in design and development of risk models is desirable.
• Strong experience in Scorecard Development, application of Machine Learning Models using techniques such as Xgboost, Light GBM, Random Forest, Logistic Regression, Decision Tree, Neural Networks etc.,
• Strong programming skills with the ability conduct research utilizing Python and Pyspark to manipulate data and conduct statistical analysis
• Strong SQL skills and experience working with large datasets
• Strong client collaboration skills, including the ability to build and maintain relationships with clients
• Ability to effectively communicate complex ideas to both a technical and non-technical audience

Preferred Skills:

• Strong analytical mind and business acumen, especially in Financial Services Industry
• Proven working experience in applying modern machine learning techniques
• Passionate on stay abreast of cutting-edge ML algorithms, with good grasp of ML explain-ability methods
• Strong technical writing skills
• Familiarity with processing of unstructured data is a plus
• Experience in NLP / LLMs is mandatory

Hiring: Junior Data Scientist (AI/ML) Trivandrum Immediate Joiners Preferred

We are looking for a passionate Junior Data Scientist with hands-on experience in AI/ML, Python, and Cloud technologies to join our growing team in Trivandrum.

Experience: 1.6 - 3 Years

Location: Trivandrum

Notice Period: Max 30 Days (Immediate joiners preferred)

Key Skills:

• Strong foundation in Data Science & Machine Learning
• Proficiency in Python (NumPy, Pandas, Scikit-learn, etc.)
• Exposure to Cloud Platforms (AWS/Azure/GCP)
• Knowledge of data preprocessing, model building, and evaluation
• Basic understanding of AI concepts and real-world applications

Responsibilities:

• Build, test, and deploy ML models
• Work with structured/unstructured data
• Collaborate with cross-functional teams to deliver data-driven solutions
• Optimize models for performance and scalability

If you're eager to grow your career in Data Science and work on exciting AI-driven projects, we'd love to hear from you!

Share your resume or tag someone who might be a great fit!

Job Title: Python Tutor/Trainer (2 Years Experience)

Location: Noida Sector 63

Interview Availability : Face to Face interview only

Working Days: 6 Days a Week

Job Type: Full-Time

Job Summary:

We are seeking a passionate and experienced Python Tutor/Trainer with a minimum of 2 years of hands-on teaching/training experience. The ideal candidate will have a strong foundation in Python programming and a proven track record of training students or professionals effectively. This role involves delivering classroom and/or online sessions, creating course content, and mentoring learners.

Key Responsibilities:

Deliver comprehensive training in Python to students, professionals, or corporate clients.

Develop and update course materials, assignments, and assessments as per industry standards.

Explain complex concepts in a clear and engaging manner.

Provide hands-on coding sessions, real-time project guidance, and doubt-clearing sessions.

Monitor student progress and provide regular feedback.

Keep up to date with the latest Python technologies, tools, and trends.

Conduct tests and evaluations to assess learner understanding and knowledge retention.

Support curriculum development and course design improvements.

Requirements:

Minimum 2 years of experience as a Python and AI Trainer or in a similar training/teaching role.

Strong knowledge of core and advanced Python concepts.

Experience with frameworks like Django, Flask, python core, oops, development, html, css or FastAPI is a plus.

Experience with Machine learning, Deep learning, NLP, Computer vision.

Experience with Generative AI would be plus.

Excellent communication and presentation skills.

Ability to manage a classroom or live online session efficiently.

Experience with creating training materials, projects, and assessments.

Bachelor's degree in Computer Science, IT, or a related field (preferred).

Additional Details:

Work Schedule: 6 days a week

Job Types: Full-time, Permanent

Work Location: In person

Phone no. -

Mail Id -

Sasi Creative School of Architecture and Design (SCSAD) is expanding its interdisciplinary B.Des program with a new program in B.Design - Data Visualisation and Data Science where students build data stories, dashboards, and interactive visualizations. We are seeking faculty members to join our top-tier design institution in Coimbatore, Tamil Nadu.

Minimum Requirements:

• Education: B.Des / B.Tech / B.E (Degree specializing in Data Science or Data Visualization preferred).
• Experience: 3+ years of professional experience.
• Core Skills: Mandatory knowledge of Data Science; ability to teach through projects, labs, and portfolio mentoring.

Role Specializations:

• Studio: Data Visualization & Information Design (storytelling, visual systems).
• Applied: Data Science / ML (Python analytics, modeling, evaluation).
• Engineering: Interactive visualizations, dashboards, web implementations.
• Ethics: Statistics, research methods, and responsible storytelling bias.

Technical Stack (Expected):

• Programming: Python (Pandas, NumPy, Matplotlib), SQL, Jupyter/Colab.
• Visualization/BI: Tableau, Power BI, Flourish, Datawrapper.
• Design: Figma, Adobe Illustrator (After Effects is a plus).
• Web (Plus): D3.js, HTML/CSS/JavaScript (React).

Application Process: Please submit your application via the LinkedIn "Apply" button. Ensure your profile includes a link to your portfolio (design/viz) or GitHub (tech/data) and a note detailing the specific tools and courses you are qualified to teach.

About Us: Kauvery Group of Hospitals launched its first hospital more than two decades ago. The founders of Kauvery Hospital were determined on creating world-class healthcare facilities that shall be affordable. The founding doctors set off on this dream in 1999 with a 30-bedded hospital in Trichy, focus on offering the best-in-class healthcare, with a personal touch. This was a very new concept in a tier 2 city like Trichy which lacked a tertiary care hospital at the time. Today, Kauvery is a multi-specialty hospital chain with 2250+ beds in six locations including Trichy, Chennai, Salem, Hosur, Tirunelveli and Bengaluru. With twelve hospitals and a workforce of over 8000+, Kauvery's mission is to provide exemplary secondary and tertiary care

Our Vision

To be the most respected and trusted healthcare provider

Our Mission

To make great healthcare affordable

Our Values

• Continual Improvement
• Heartfelt Personal Touch
• Ethical
• Empathetic Care
• Real Accountability
• Service Excellence

Job Title: Data Science Technologist (AI Integration)

Department: Information Technology

Location: Kauvery Hospital, Trichy!

Reporting To: In-charge

Qualification: BSC Computer Science / BCA /MCA / M.Sc. Computer Science / Data Science

Experience:

• 1-3 years of experience in Data Science, Data Analytics, or Artificial Intelligence related roles (freshers with strong AI/ML project exposure may also apply).

Key Responsibilities:

• Develop and implement AI and Machine Learning models to support business decision-making.
• Work with hospital or organizational data to create predictive analytics and performance dashboards.
• Build and maintain data pipelines and databases for efficient data processing.
• Develop data visualization reports using tools such as Power BI, Tableau, or Python libraries.
• Collaborate with various departments to understand data requirements and provide analytical solutions.
• Support automation initiatives using AI tools and algorithms.
• Ensure data security, accuracy, and compliance with organizational standards.

Technical Skills Required:

• Strong knowledge of Python / R programming for data analysis.
• Experience with Machine Learning algorithms and AI concepts.
• Knowledge of SQL, data mining, and data modeling.
• Familiarity with data visualization tools such as Power BI or Tableau.
• Understanding of statistics, predictive analytics, and data interpretation.
• Exposure to AI frameworks such as TensorFlow / Scikit-learn will be an added advantage.

Soft Skills:

• Strong analytical and problem-solving skills.
• Good communication and presentation skills.
• Ability to work collaboratively with cross-functional teams.
• Attention to detail and data accuracy.

Key Responsibilities

• Perform ad-hoc analysis on large-scale data to extract actionable insights and support data-driven decision making.
• Develop a strong understanding of the credit risk modelling lifecycle, including data preparation, model development, validation, and monitoring.
• Build foundational knowledge of machine learning techniques and their applications in credit risk - including classification, regression, and ensemble methods.
• Support validation of existing credit risk models and assist in developing or enhancing models under the guidance of senior team members.
• Extract, manipulate, and explore large datasets using Python (pandas and related data analysis libraries) and SQL to ensure data quality and integrity.
• Communicate analytical findings and model results clearly to both technical and non-technical stakeholders, including leadership presentations

Required Skills & Experience

• Upto 4 years of experience in data science, credit risk analytics, or a related quantitative role.
• Hands-on proficiency in Python - particularly pandas, NumPy, and data analysis libraries - for data manipulation and exploratory analysis.
• Working knowledge of SQL for data extraction and querying large datasets.
• Foundational understanding of machine learning concepts and modelling techniques (e.g., logistic regression, decision trees, gradient boosting).
• Exposure to credit bureau data, credit risk concepts, or consumer lending data is a plus.
• Strong communication skills with the ability to present analysis and findings clearly to both technical teams and leadership.

Preferred / Additional Skills

• Familiarity with data visualization tools such as Power BI for presenting analytical insights.
• Basic awareness of cloud-based data platforms (AWS) and big data tools.
• Prior academic or project experience with credit scoring, risk modelling, or financial data analysis.

Educational Qualifications

• Degree from a top-tier institution; Particularly in a quantitative field (e.g., computer science, data science, engineering, economics, mathematics, etc.). Advanced degree preferred.

Key Responsibilities

• Perform ad-hoc analysis on large-scale data to extract actionable insights and support data-driven decision making.
• Develop a strong understanding of the credit risk modelling lifecycle, including data preparation, model development, validation, and monitoring.
• Build foundational knowledge of machine learning techniques and their applications in credit risk - including classification, regression, and ensemble methods.
• Support validation of existing credit risk models and assist in developing or enhancing models under the guidance of senior team members.
• Extract, manipulate, and explore large datasets using Python (pandas and related data analysis libraries) and SQL to ensure data quality and integrity.
• Communicate analytical findings and model results clearly to both technical and non-technical stakeholders, including leadership presentations

Required Skills & Experience

• Upto 4 years of experience in data science, credit risk analytics, or a related quantitative role.
• Hands-on proficiency in Python - particularly pandas, NumPy, and data analysis libraries - for data manipulation and exploratory analysis.
• Working knowledge of SQL for data extraction and querying large datasets.
• Foundational understanding of machine learning concepts and modelling techniques (e.g., logistic regression, decision trees, gradient boosting).
• Exposure to credit bureau data, credit risk concepts, or consumer lending data is a plus.
• Strong communication skills with the ability to present analysis and findings clearly to both technical teams and leadership.

Preferred / Additional Skills

• Familiarity with data visualization tools such as Power BI for presenting analytical insights.
• Basic awareness of cloud-based data platforms (AWS) and big data tools.
• Prior academic or project experience with credit scoring, risk modelling, or financial data analysis.

Educational Qualifications

• Degree from a top-tier institution; Particularly in a quantitative field (e.g., computer science, data science, engineering, economics, mathematics, etc.). Advanced degree preferred.

Key Responsibilities

• Perform ad-hoc analysis on large-scale data to extract actionable insights and support data-driven decision making.
• Develop a strong understanding of the credit risk modelling lifecycle, including data preparation, model development, validation, and monitoring.
• Build foundational knowledge of machine learning techniques and their applications in credit risk - including classification, regression, and ensemble methods.
• Support validation of existing credit risk models and assist in developing or enhancing models under the guidance of senior team members.
• Extract, manipulate, and explore large datasets using Python (pandas and related data analysis libraries) and SQL to ensure data quality and integrity.
• Communicate analytical findings and model results clearly to both technical and non-technical stakeholders, including leadership presentations

Required Skills & Experience

• Upto 4 years of experience in data science, credit risk analytics, or a related quantitative role.
• Hands-on proficiency in Python - particularly pandas, NumPy, and data analysis libraries - for data manipulation and exploratory analysis.
• Working knowledge of SQL for data extraction and querying large datasets.
• Foundational understanding of machine learning concepts and modelling techniques (e.g., logistic regression, decision trees, gradient boosting).
• Exposure to credit bureau data, credit risk concepts, or consumer lending data is a plus.
• Strong communication skills with the ability to present analysis and findings clearly to both technical teams and leadership.

Preferred / Additional Skills

• Familiarity with data visualization tools such as Power BI for presenting analytical insights.
• Basic awareness of cloud-based data platforms (AWS) and big data tools.
• Prior academic or project experience with credit scoring, risk modelling, or financial data analysis.

Educational Qualifications

• Degree from a top-tier institution; Particularly in a quantitative field (e.g., computer science, data science, engineering, economics, mathematics, etc.). Advanced degree preferred.

Key Responsibilities

• Perform ad-hoc analysis on large-scale data to extract actionable insights and support data-driven decision making.
• Develop a strong understanding of the credit risk modelling lifecycle, including data preparation, model development, validation, and monitoring.
• Build foundational knowledge of machine learning techniques and their applications in credit risk - including classification, regression, and ensemble methods.
• Support validation of existing credit risk models and assist in developing or enhancing models under the guidance of senior team members.
• Extract, manipulate, and explore large datasets using Python (pandas and related data analysis libraries) and SQL to ensure data quality and integrity.
• Communicate analytical findings and model results clearly to both technical and non-technical stakeholders, including leadership presentations

Required Skills & Experience

• Upto 4 years of experience in data science, credit risk analytics, or a related quantitative role.
• Hands-on proficiency in Python - particularly pandas, NumPy, and data analysis libraries - for data manipulation and exploratory analysis.
• Working knowledge of SQL for data extraction and querying large datasets.
• Foundational understanding of machine learning concepts and modelling techniques (e.g., logistic regression, decision trees, gradient boosting).
• Exposure to credit bureau data, credit risk concepts, or consumer lending data is a plus.
• Strong communication skills with the ability to present analysis and findings clearly to both technical teams and leadership.

Preferred / Additional Skills

• Familiarity with data visualization tools such as Power BI for presenting analytical insights.
• Basic awareness of cloud-based data platforms (AWS) and big data tools.
• Prior academic or project experience with credit scoring, risk modelling, or financial data analysis.

Educational Qualifications

• Degree from a top-tier institution; Particularly in a quantitative field (e.g., computer science, data science, engineering, economics, mathematics, etc.). Advanced degree preferred.

Key Responsibilities

• Perform ad-hoc analysis on large-scale data to extract actionable insights and support data-driven decision making.
• Develop a strong understanding of the credit risk modelling lifecycle, including data preparation, model development, validation, and monitoring.
• Build foundational knowledge of machine learning techniques and their applications in credit risk - including classification, regression, and ensemble methods.
• Support validation of existing credit risk models and assist in developing or enhancing models under the guidance of senior team members.
• Extract, manipulate, and explore large datasets using Python (pandas and related data analysis libraries) and SQL to ensure data quality and integrity.
• Communicate analytical findings and model results clearly to both technical and non-technical stakeholders, including leadership presentations

Required Skills & Experience

• Upto 4 years of experience in data science, credit risk analytics, or a related quantitative role.
• Hands-on proficiency in Python - particularly pandas, NumPy, and data analysis libraries - for data manipulation and exploratory analysis.
• Working knowledge of SQL for data extraction and querying large datasets.
• Foundational understanding of machine learning concepts and modelling techniques (e.g., logistic regression, decision trees, gradient boosting).
• Exposure to credit bureau data, credit risk concepts, or consumer lending data is a plus.
• Strong communication skills with the ability to present analysis and findings clearly to both technical teams and leadership.

Preferred / Additional Skills

• Familiarity with data visualization tools such as Power BI for presenting analytical insights.
• Basic awareness of cloud-based data platforms (AWS) and big data tools.
• Prior academic or project experience with credit scoring, risk modelling, or financial data analysis.

Educational Qualifications

• Degree from a top-tier institution; Particularly in a quantitative field (e.g., computer science, data science, engineering, economics, mathematics, etc.). Advanced degree preferred.

Key Responsibilities

• Perform ad-hoc analysis on large-scale data to extract actionable insights and support data-driven decision making.
• Develop a strong understanding of the credit risk modelling lifecycle, including data preparation, model development, validation, and monitoring.
• Build foundational knowledge of machine learning techniques and their applications in credit risk - including classification, regression, and ensemble methods.
• Support validation of existing credit risk models and assist in developing or enhancing models under the guidance of senior team members.
• Extract, manipulate, and explore large datasets using Python (pandas and related data analysis libraries) and SQL to ensure data quality and integrity.
• Communicate analytical findings and model results clearly to both technical and non-technical stakeholders, including leadership presentations

Required Skills & Experience

• Upto 4 years of experience in data science, credit risk analytics, or a related quantitative role.
• Hands-on proficiency in Python - particularly pandas, NumPy, and data analysis libraries - for data manipulation and exploratory analysis.
• Working knowledge of SQL for data extraction and querying large datasets.
• Foundational understanding of machine learning concepts and modelling techniques (e.g., logistic regression, decision trees, gradient boosting).
• Exposure to credit bureau data, credit risk concepts, or consumer lending data is a plus.
• Strong communication skills with the ability to present analysis and findings clearly to both technical teams and leadership.

Preferred / Additional Skills

• Familiarity with data visualization tools such as Power BI for presenting analytical insights.
• Basic awareness of cloud-based data platforms (AWS) and big data tools.
• Prior academic or project experience with credit scoring, risk modelling, or financial data analysis.

Educational Qualifications

• Degree from a top-tier institution; Particularly in a quantitative field (e.g., computer science, data science, engineering, economics, mathematics, etc.). Advanced degree preferred.

Key Responsibilities

• Perform ad-hoc analysis on large-scale data to extract actionable insights and support data-driven decision making.
• Develop a strong understanding of the credit risk modelling lifecycle, including data preparation, model development, validation, and monitoring.
• Build foundational knowledge of machine learning techniques and their applications in credit risk - including classification, regression, and ensemble methods.
• Support validation of existing credit risk models and assist in developing or enhancing models under the guidance of senior team members.
• Extract, manipulate, and explore large datasets using Python (pandas and related data analysis libraries) and SQL to ensure data quality and integrity.
• Communicate analytical findings and model results clearly to both technical and non-technical stakeholders, including leadership presentations

Required Skills & Experience

• Upto 4 years of experience in data science, credit risk analytics, or a related quantitative role.
• Hands-on proficiency in Python - particularly pandas, NumPy, and data analysis libraries - for data manipulation and exploratory analysis.
• Working knowledge of SQL for data extraction and querying large datasets.
• Foundational understanding of machine learning concepts and modelling techniques (e.g., logistic regression, decision trees, gradient boosting).
• Exposure to credit bureau data, credit risk concepts, or consumer lending data is a plus.
• Strong communication skills with the ability to present analysis and findings clearly to both technical teams and leadership.

Preferred / Additional Skills

• Familiarity with data visualization tools such as Power BI for presenting analytical insights.
• Basic awareness of cloud-based data platforms (AWS) and big data tools.
• Prior academic or project experience with credit scoring, risk modelling, or financial data analysis.

Educational Qualifications

• Degree from a top-tier institution; Particularly in a quantitative field (e.g., computer science, data science, engineering, economics, mathematics, etc.). Advanced degree preferred.

Key Responsibilities

• Perform ad-hoc analysis on large-scale data to extract actionable insights and support data-driven decision making.
• Develop a strong understanding of the credit risk modelling lifecycle, including data preparation, model development, validation, and monitoring.
• Build foundational knowledge of machine learning techniques and their applications in credit risk - including classification, regression, and ensemble methods.
• Support validation of existing credit risk models and assist in developing or enhancing models under the guidance of senior team members.
• Extract, manipulate, and explore large datasets using Python (pandas and related data analysis libraries) and SQL to ensure data quality and integrity.
• Communicate analytical findings and model results clearly to both technical and non-technical stakeholders, including leadership presentations

Required Skills & Experience

• Upto 4 years of experience in data science, credit risk analytics, or a related quantitative role.
• Hands-on proficiency in Python - particularly pandas, NumPy, and data analysis libraries - for data manipulation and exploratory analysis.
• Working knowledge of SQL for data extraction and querying large datasets.
• Foundational understanding of machine learning concepts and modelling techniques (e.g., logistic regression, decision trees, gradient boosting).
• Exposure to credit bureau data, credit risk concepts, or consumer lending data is a plus.
• Strong communication skills with the ability to present analysis and findings clearly to both technical teams and leadership.

Preferred / Additional Skills

• Familiarity with data visualization tools such as Power BI for presenting analytical insights.
• Basic awareness of cloud-based data platforms (AWS) and big data tools.
• Prior academic or project experience with credit scoring, risk modelling, or financial data analysis.

Educational Qualifications

• Degree from a top-tier institution; Particularly in a quantitative field (e.g., computer science, data science, engineering, economics, mathematics, etc.). Advanced degree preferred.

Key Responsibilities

• Perform ad-hoc analysis on large-scale data to extract actionable insights and support data-driven decision making.
• Develop a strong understanding of the credit risk modelling lifecycle, including data preparation, model development, validation, and monitoring.
• Build foundational knowledge of machine learning techniques and their applications in credit risk - including classification, regression, and ensemble methods.
• Support validation of existing credit risk models and assist in developing or enhancing models under the guidance of senior team members.
• Extract, manipulate, and explore large datasets using Python (pandas and related data analysis libraries) and SQL to ensure data quality and integrity.
• Communicate analytical findings and model results clearly to both technical and non-technical stakeholders, including leadership presentations

Required Skills & Experience

• Upto 4 years of experience in data science, credit risk analytics, or a related quantitative role.
• Hands-on proficiency in Python - particularly pandas, NumPy, and data analysis libraries - for data manipulation and exploratory analysis.
• Working knowledge of SQL for data extraction and querying large datasets.
• Foundational understanding of machine learning concepts and modelling techniques (e.g., logistic regression, decision trees, gradient boosting).
• Exposure to credit bureau data, credit risk concepts, or consumer lending data is a plus.
• Strong communication skills with the ability to present analysis and findings clearly to both technical teams and leadership.

Preferred / Additional Skills

• Familiarity with data visualization tools such as Power BI for presenting analytical insights.
• Basic awareness of cloud-based data platforms (AWS) and big data tools.
• Prior academic or project experience with credit scoring, risk modelling, or financial data analysis.

Educational Qualifications

• Degree from a top-tier institution; Particularly in a quantitative field (e.g., computer science, data science, engineering, economics, mathematics, etc.). Advanced degree preferred.

Key Responsibilities

• Perform ad-hoc analysis on large-scale data to extract actionable insights and support data-driven decision making.
• Develop a strong understanding of the credit risk modelling lifecycle, including data preparation, model development, validation, and monitoring.
• Build foundational knowledge of machine learning techniques and their applications in credit risk - including classification, regression, and ensemble methods.
• Support validation of existing credit risk models and assist in developing or enhancing models under the guidance of senior team members.
• Extract, manipulate, and explore large datasets using Python (pandas and related data analysis libraries) and SQL to ensure data quality and integrity.
• Communicate analytical findings and model results clearly to both technical and non-technical stakeholders, including leadership presentations

Required Skills & Experience

• Upto 4 years of experience in data science, credit risk analytics, or a related quantitative role.
• Hands-on proficiency in Python - particularly pandas, NumPy, and data analysis libraries - for data manipulation and exploratory analysis.
• Working knowledge of SQL for data extraction and querying large datasets.
• Foundational understanding of machine learning concepts and modelling techniques (e.g., logistic regression, decision trees, gradient boosting).
• Exposure to credit bureau data, credit risk concepts, or consumer lending data is a plus.
• Strong communication skills with the ability to present analysis and findings clearly to both technical teams and leadership.

Preferred / Additional Skills

• Familiarity with data visualization tools such as Power BI for presenting analytical insights.
• Basic awareness of cloud-based data platforms (AWS) and big data tools.
• Prior academic or project experience with credit scoring, risk modelling, or financial data analysis.

Educational Qualifications

• Degree from a top-tier institution; Particularly in a quantitative field (e.g., computer science, data science, engineering, economics, mathematics, etc.). Advanced degree preferred.

Key Responsibilities

• Perform ad-hoc analysis on large-scale data to extract actionable insights and support data-driven decision making.
• Develop a strong understanding of the credit risk modelling lifecycle, including data preparation, model development, validation, and monitoring.
• Build foundational knowledge of machine learning techniques and their applications in credit risk - including classification, regression, and ensemble methods.
• Support validation of existing credit risk models and assist in developing or enhancing models under the guidance of senior team members.
• Extract, manipulate, and explore large datasets using Python (pandas and related data analysis libraries) and SQL to ensure data quality and integrity.
• Communicate analytical findings and model results clearly to both technical and non-technical stakeholders, including leadership presentations

Required Skills & Experience

• Upto 4 years of experience in data science, credit risk analytics, or a related quantitative role.
• Hands-on proficiency in Python - particularly pandas, NumPy, and data analysis libraries - for data manipulation and exploratory analysis.
• Working knowledge of SQL for data extraction and querying large datasets.
• Foundational understanding of machine learning concepts and modelling techniques (e.g., logistic regression, decision trees, gradient boosting).
• Exposure to credit bureau data, credit risk concepts, or consumer lending data is a plus.
• Strong communication skills with the ability to present analysis and findings clearly to both technical teams and leadership.

Preferred / Additional Skills

• Familiarity with data visualization tools such as Power BI for presenting analytical insights.
• Basic awareness of cloud-based data platforms (AWS) and big data tools.
• Prior academic or project experience with credit scoring, risk modelling, or financial data analysis.

Educational Qualifications

• Degree from a top-tier institution; Particularly in a quantitative field (e.g., computer science, data science, engineering, economics, mathematics, etc.). Advanced degree preferred.

Key Responsibilities

• Perform ad-hoc analysis on large-scale data to extract actionable insights and support data-driven decision making.
• Develop a strong understanding of the credit risk modelling lifecycle, including data preparation, model development, validation, and monitoring.
• Build foundational knowledge of machine learning techniques and their applications in credit risk - including classification, regression, and ensemble methods.
• Support validation of existing credit risk models and assist in developing or enhancing models under the guidance of senior team members.
• Extract, manipulate, and explore large datasets using Python (pandas and related data analysis libraries) and SQL to ensure data quality and integrity.
• Communicate analytical findings and model results clearly to both technical and non-technical stakeholders, including leadership presentations

Required Skills & Experience

• Upto 4 years of experience in data science, credit risk analytics, or a related quantitative role.
• Hands-on proficiency in Python - particularly pandas, NumPy, and data analysis libraries - for data manipulation and exploratory analysis.
• Working knowledge of SQL for data extraction and querying large datasets.
• Foundational understanding of machine learning concepts and modelling techniques (e.g., logistic regression, decision trees, gradient boosting).
• Exposure to credit bureau data, credit risk concepts, or consumer lending data is a plus.
• Strong communication skills with the ability to present analysis and findings clearly to both technical teams and leadership.

Preferred / Additional Skills

• Familiarity with data visualization tools such as Power BI for presenting analytical insights.
• Basic awareness of cloud-based data platforms (AWS) and big data tools.
• Prior academic or project experience with credit scoring, risk modelling, or financial data analysis.

Educational Qualifications

• Degree from a top-tier institution; Particularly in a quantitative field (e.g., computer science, data science, engineering, economics, mathematics, etc.). Advanced degree preferred.

Key Responsibilities

• Perform ad-hoc analysis on large-scale data to extract actionable insights and support data-driven decision making.
• Develop a strong understanding of the credit risk modelling lifecycle, including data preparation, model development, validation, and monitoring.
• Build foundational knowledge of machine learning techniques and their applications in credit risk - including classification, regression, and ensemble methods.
• Support validation of existing credit risk models and assist in developing or enhancing models under the guidance of senior team members.
• Extract, manipulate, and explore large datasets using Python (pandas and related data analysis libraries) and SQL to ensure data quality and integrity.
• Communicate analytical findings and model results clearly to both technical and non-technical stakeholders, including leadership presentations

Required Skills & Experience

• Upto 4 years of experience in data science, credit risk analytics, or a related quantitative role.
• Hands-on proficiency in Python - particularly pandas, NumPy, and data analysis libraries - for data manipulation and exploratory analysis.
• Working knowledge of SQL for data extraction and querying large datasets.
• Foundational understanding of machine learning concepts and modelling techniques (e.g., logistic regression, decision trees, gradient boosting).
• Exposure to credit bureau data, credit risk concepts, or consumer lending data is a plus.
• Strong communication skills with the ability to present analysis and findings clearly to both technical teams and leadership.

Preferred / Additional Skills

• Familiarity with data visualization tools such as Power BI for presenting analytical insights.
• Basic awareness of cloud-based data platforms (AWS) and big data tools.
• Prior academic or project experience with credit scoring, risk modelling, or financial data analysis.

Educational Qualifications

• Degree from a top-tier institution; Particularly in a quantitative field (e.g., computer science, data science, engineering, economics, mathematics, etc.). Advanced degree preferred.

Key Responsibilities

• Perform ad-hoc analysis on large-scale data to extract actionable insights and support data-driven decision making.
• Develop a strong understanding of the credit risk modelling lifecycle, including data preparation, model development, validation, and monitoring.
• Build foundational knowledge of machine learning techniques and their applications in credit risk - including classification, regression, and ensemble methods.
• Support validation of existing credit risk models and assist in developing or enhancing models under the guidance of senior team members.
• Extract, manipulate, and explore large datasets using Python (pandas and related data analysis libraries) and SQL to ensure data quality and integrity.
• Communicate analytical findings and model results clearly to both technical and non-technical stakeholders, including leadership presentations

Required Skills & Experience

• Upto 4 years of experience in data science, credit risk analytics, or a related quantitative role.
• Hands-on proficiency in Python - particularly pandas, NumPy, and data analysis libraries - for data manipulation and exploratory analysis.
• Working knowledge of SQL for data extraction and querying large datasets.
• Foundational understanding of machine learning concepts and modelling techniques (e.g., logistic regression, decision trees, gradient boosting).
• Exposure to credit bureau data, credit risk concepts, or consumer lending data is a plus.
• Strong communication skills with the ability to present analysis and findings clearly to both technical teams and leadership.

Preferred / Additional Skills

• Familiarity with data visualization tools such as Power BI for presenting analytical insights.
• Basic awareness of cloud-based data platforms (AWS) and big data tools.
• Prior academic or project experience with credit scoring, risk modelling, or financial data analysis.

Educational Qualifications

• Degree from a top-tier institution; Particularly in a quantitative field (e.g., computer science, data science, engineering, economics, mathematics, etc.). Advanced degree preferred.

Key Responsibilities

• Perform ad-hoc analysis on large-scale data to extract actionable insights and support data-driven decision making.
• Develop a strong understanding of the credit risk modelling lifecycle, including data preparation, model development, validation, and monitoring.
• Build foundational knowledge of machine learning techniques and their applications in credit risk - including classification, regression, and ensemble methods.
• Support validation of existing credit risk models and assist in developing or enhancing models under the guidance of senior team members.
• Extract, manipulate, and explore large datasets using Python (pandas and related data analysis libraries) and SQL to ensure data quality and integrity.
• Communicate analytical findings and model results clearly to both technical and non-technical stakeholders, including leadership presentations

Required Skills & Experience

• Upto 4 years of experience in data science, credit risk analytics, or a related quantitative role.
• Hands-on proficiency in Python - particularly pandas, NumPy, and data analysis libraries - for data manipulation and exploratory analysis.
• Working knowledge of SQL for data extraction and querying large datasets.
• Foundational understanding of machine learning concepts and modelling techniques (e.g., logistic regression, decision trees, gradient boosting).
• Exposure to credit bureau data, credit risk concepts, or consumer lending data is a plus.
• Strong communication skills with the ability to present analysis and findings clearly to both technical teams and leadership.

Preferred / Additional Skills

• Familiarity with data visualization tools such as Power BI for presenting analytical insights.
• Basic awareness of cloud-based data platforms (AWS) and big data tools.
• Prior academic or project experience with credit scoring, risk modelling, or financial data analysis.

Educational Qualifications

• Degree from a top-tier institution; Particularly in a quantitative field (e.g., computer science, data science, engineering, economics, mathematics, etc.). Advanced degree preferred.

Key Responsibilities

• Perform ad-hoc analysis on large-scale data to extract actionable insights and support data-driven decision making.
• Develop a strong understanding of the credit risk modelling lifecycle, including data preparation, model development, validation, and monitoring.
• Build foundational knowledge of machine learning techniques and their applications in credit risk - including classification, regression, and ensemble methods.
• Support validation of existing credit risk models and assist in developing or enhancing models under the guidance of senior team members.
• Extract, manipulate, and explore large datasets using Python (pandas and related data analysis libraries) and SQL to ensure data quality and integrity.
• Communicate analytical findings and model results clearly to both technical and non-technical stakeholders, including leadership presentations

Required Skills & Experience

• Upto 4 years of experience in data science, credit risk analytics, or a related quantitative role.
• Hands-on proficiency in Python - particularly pandas, NumPy, and data analysis libraries - for data manipulation and exploratory analysis.
• Working knowledge of SQL for data extraction and querying large datasets.
• Foundational understanding of machine learning concepts and modelling techniques (e.g., logistic regression, decision trees, gradient boosting).
• Exposure to credit bureau data, credit risk concepts, or consumer lending data is a plus.
• Strong communication skills with the ability to present analysis and findings clearly to both technical teams and leadership.

Preferred / Additional Skills

• Familiarity with data visualization tools such as Power BI for presenting analytical insights.
• Basic awareness of cloud-based data platforms (AWS) and big data tools.
• Prior academic or project experience with credit scoring, risk modelling, or financial data analysis.

Educational Qualifications

• Degree from a top-tier institution; Particularly in a quantitative field (e.g., computer science, data science, engineering, economics, mathematics, etc.). Advanced degree preferred.

Key Responsibilities

• Perform ad-hoc analysis on large-scale data to extract actionable insights and support data-driven decision making.
• Develop a strong understanding of the credit risk modelling lifecycle, including data preparation, model development, validation, and monitoring.
• Build foundational knowledge of machine learning techniques and their applications in credit risk - including classification, regression, and ensemble methods.
• Support validation of existing credit risk models and assist in developing or enhancing models under the guidance of senior team members.
• Extract, manipulate, and explore large datasets using Python (pandas and related data analysis libraries) and SQL to ensure data quality and integrity.
• Communicate analytical findings and model results clearly to both technical and non-technical stakeholders, including leadership presentations

Required Skills & Experience

• Upto 4 years of experience in data science, credit risk analytics, or a related quantitative role.
• Hands-on proficiency in Python - particularly pandas, NumPy, and data analysis libraries - for data manipulation and exploratory analysis.
• Working knowledge of SQL for data extraction and querying large datasets.
• Foundational understanding of machine learning concepts and modelling techniques (e.g., logistic regression, decision trees, gradient boosting).
• Exposure to credit bureau data, credit risk concepts, or consumer lending data is a plus.
• Strong communication skills with the ability to present analysis and findings clearly to both technical teams and leadership.

Preferred / Additional Skills

• Familiarity with data visualization tools such as Power BI for presenting analytical insights.
• Basic awareness of cloud-based data platforms (AWS) and big data tools.
• Prior academic or project experience with credit scoring, risk modelling, or financial data analysis.

Educational Qualifications

• Degree from a top-tier institution; Particularly in a quantitative field (e.g., computer science, data science, engineering, economics, mathematics, etc.). Advanced degree preferred.

Key Responsibilities

• Perform ad-hoc analysis on large-scale data to extract actionable insights and support data-driven decision making.
• Develop a strong understanding of the credit risk modelling lifecycle, including data preparation, model development, validation, and monitoring.
• Build foundational knowledge of machine learning techniques and their applications in credit risk - including classification, regression, and ensemble methods.
• Support validation of existing credit risk models and assist in developing or enhancing models under the guidance of senior team members.
• Extract, manipulate, and explore large datasets using Python (pandas and related data analysis libraries) and SQL to ensure data quality and integrity.
• Communicate analytical findings and model results clearly to both technical and non-technical stakeholders, including leadership presentations

Required Skills & Experience

• Upto 4 years of experience in data science, credit risk analytics, or a related quantitative role.
• Hands-on proficiency in Python - particularly pandas, NumPy, and data analysis libraries - for data manipulation and exploratory analysis.
• Working knowledge of SQL for data extraction and querying large datasets.
• Foundational understanding of machine learning concepts and modelling techniques (e.g., logistic regression, decision trees, gradient boosting).
• Exposure to credit bureau data, credit risk concepts, or consumer lending data is a plus.
• Strong communication skills with the ability to present analysis and findings clearly to both technical teams and leadership.

Preferred / Additional Skills

• Familiarity with data visualization tools such as Power BI for presenting analytical insights.
• Basic awareness of cloud-based data platforms (AWS) and big data tools.
• Prior academic or project experience with credit scoring, risk modelling, or financial data analysis.

Educational Qualifications

• Degree from a top-tier institution; Particularly in a quantitative field (e.g., computer science, data science, engineering, economics, mathematics, etc.). Advanced degree preferred.

Key Responsibilities

• Perform ad-hoc analysis on large-scale data to extract actionable insights and support data-driven decision making.
• Develop a strong understanding of the credit risk modelling lifecycle, including data preparation, model development, validation, and monitoring.
• Build foundational knowledge of machine learning techniques and their applications in credit risk - including classification, regression, and ensemble methods.
• Support validation of existing credit risk models and assist in developing or enhancing models under the guidance of senior team members.
• Extract, manipulate, and explore large datasets using Python (pandas and related data analysis libraries) and SQL to ensure data quality and integrity.
• Communicate analytical findings and model results clearly to both technical and non-technical stakeholders, including leadership presentations

Required Skills & Experience

• Upto 4 years of experience in data science, credit risk analytics, or a related quantitative role.
• Hands-on proficiency in Python - particularly pandas, NumPy, and data analysis libraries - for data manipulation and exploratory analysis.
• Working knowledge of SQL for data extraction and querying large datasets.
• Foundational understanding of machine learning concepts and modelling techniques (e.g., logistic regression, decision trees, gradient boosting).
• Exposure to credit bureau data, credit risk concepts, or consumer lending data is a plus.
• Strong communication skills with the ability to present analysis and findings clearly to both technical teams and leadership.

Preferred / Additional Skills

• Familiarity with data visualization tools such as Power BI for presenting analytical insights.
• Basic awareness of cloud-based data platforms (AWS) and big data tools.
• Prior academic or project experience with credit scoring, risk modelling, or financial data analysis.

Educational Qualifications

• Degree from a top-tier institution; Particularly in a quantitative field (e.g., computer science, data science, engineering, economics, mathematics, etc.). Advanced degree preferred.

Key Responsibilities

• Perform ad-hoc analysis on large-scale data to extract actionable insights and support data-driven decision making.
• Develop a strong understanding of the credit risk modelling lifecycle, including data preparation, model development, validation, and monitoring.
• Build foundational knowledge of machine learning techniques and their applications in credit risk - including classification, regression, and ensemble methods.
• Support validation of existing credit risk models and assist in developing or enhancing models under the guidance of senior team members.
• Extract, manipulate, and explore large datasets using Python (pandas and related data analysis libraries) and SQL to ensure data quality and integrity.
• Communicate analytical findings and model results clearly to both technical and non-technical stakeholders, including leadership presentations

Required Skills & Experience

• Upto 4 years of experience in data science, credit risk analytics, or a related quantitative role.
• Hands-on proficiency in Python - particularly pandas, NumPy, and data analysis libraries - for data manipulation and exploratory analysis.
• Working knowledge of SQL for data extraction and querying large datasets.
• Foundational understanding of machine learning concepts and modelling techniques (e.g., logistic regression, decision trees, gradient boosting).
• Exposure to credit bureau data, credit risk concepts, or consumer lending data is a plus.
• Strong communication skills with the ability to present analysis and findings clearly to both technical teams and leadership.

Preferred / Additional Skills

• Familiarity with data visualization tools such as Power BI for presenting analytical insights.
• Basic awareness of cloud-based data platforms (AWS) and big data tools.
• Prior academic or project experience with credit scoring, risk modelling, or financial data analysis.

Educational Qualifications

• Degree from a top-tier institution; Particularly in a quantitative field (e.g., computer science, data science, engineering, economics, mathematics, etc.). Advanced degree preferred.

Key Responsibilities

• Perform ad-hoc analysis on large-scale data to extract actionable insights and support data-driven decision making.
• Develop a strong understanding of the credit risk modelling lifecycle, including data preparation, model development, validation, and monitoring.
• Build foundational knowledge of machine learning techniques and their applications in credit risk - including classification, regression, and ensemble methods.
• Support validation of existing credit risk models and assist in developing or enhancing models under the guidance of senior team members.
• Extract, manipulate, and explore large datasets using Python (pandas and related data analysis libraries) and SQL to ensure data quality and integrity.
• Communicate analytical findings and model results clearly to both technical and non-technical stakeholders, including leadership presentations

Required Skills & Experience

• Upto 4 years of experience in data science, credit risk analytics, or a related quantitative role.
• Hands-on proficiency in Python - particularly pandas, NumPy, and data analysis libraries - for data manipulation and exploratory analysis.
• Working knowledge of SQL for data extraction and querying large datasets.
• Foundational understanding of machine learning concepts and modelling techniques (e.g., logistic regression, decision trees, gradient boosting).
• Exposure to credit bureau data, credit risk concepts, or consumer lending data is a plus.
• Strong communication skills with the ability to present analysis and findings clearly to both technical teams and leadership.

Preferred / Additional Skills

• Familiarity with data visualization tools such as Power BI for presenting analytical insights.
• Basic awareness of cloud-based data platforms (AWS) and big data tools.
• Prior academic or project experience with credit scoring, risk modelling, or financial data analysis.

Educational Qualifications

• Degree from a top-tier institution; Particularly in a quantitative field (e.g., computer science, data science, engineering, economics, mathematics, etc.). Advanced degree preferred.

Key Responsibilities

• Perform ad-hoc analysis on large-scale data to extract actionable insights and support data-driven decision making.
• Develop a strong understanding of the credit risk modelling lifecycle, including data preparation, model development, validation, and monitoring.
• Build foundational knowledge of machine learning techniques and their applications in credit risk - including classification, regression, and ensemble methods.
• Support validation of existing credit risk models and assist in developing or enhancing models under the guidance of senior team members.
• Extract, manipulate, and explore large datasets using Python (pandas and related data analysis libraries) and SQL to ensure data quality and integrity.
• Communicate analytical findings and model results clearly to both technical and non-technical stakeholders, including leadership presentations

Required Skills & Experience

• Upto 4 years of experience in data science, credit risk analytics, or a related quantitative role.
• Hands-on proficiency in Python - particularly pandas, NumPy, and data analysis libraries - for data manipulation and exploratory analysis.
• Working knowledge of SQL for data extraction and querying large datasets.
• Foundational understanding of machine learning concepts and modelling techniques (e.g., logistic regression, decision trees, gradient boosting).
• Exposure to credit bureau data, credit risk concepts, or consumer lending data is a plus.
• Strong communication skills with the ability to present analysis and findings clearly to both technical teams and leadership.

Preferred / Additional Skills

• Familiarity with data visualization tools such as Power BI for presenting analytical insights.
• Basic awareness of cloud-based data platforms (AWS) and big data tools.
• Prior academic or project experience with credit scoring, risk modelling, or financial data analysis.

Educational Qualifications

• Degree from a top-tier institution; Particularly in a quantitative field (e.g., computer science, data science, engineering, economics, mathematics, etc.). Advanced degree preferred.

Key Responsibilities

• Perform ad-hoc analysis on large-scale data to extract actionable insights and support data-driven decision making.
• Develop a strong understanding of the credit risk modelling lifecycle, including data preparation, model development, validation, and monitoring.
• Build foundational knowledge of machine learning techniques and their applications in credit risk - including classification, regression, and ensemble methods.
• Support validation of existing credit risk models and assist in developing or enhancing models under the guidance of senior team members.
• Extract, manipulate, and explore large datasets using Python (pandas and related data analysis libraries) and SQL to ensure data quality and integrity.
• Communicate analytical findings and model results clearly to both technical and non-technical stakeholders, including leadership presentations

Required Skills & Experience

• Upto 4 years of experience in data science, credit risk analytics, or a related quantitative role.
• Hands-on proficiency in Python - particularly pandas, NumPy, and data analysis libraries - for data manipulation and exploratory analysis.
• Working knowledge of SQL for data extraction and querying large datasets.
• Foundational understanding of machine learning concepts and modelling techniques (e.g., logistic regression, decision trees, gradient boosting).
• Exposure to credit bureau data, credit risk concepts, or consumer lending data is a plus.
• Strong communication skills with the ability to present analysis and findings clearly to both technical teams and leadership.

Preferred / Additional Skills

• Familiarity with data visualization tools such as Power BI for presenting analytical insights.
• Basic awareness of cloud-based data platforms (AWS) and big data tools.
• Prior academic or project experience with credit scoring, risk modelling, or financial data analysis.

Educational Qualifications

• Degree from a top-tier institution; Particularly in a quantitative field (e.g., computer science, data science, engineering, economics, mathematics, etc.). Advanced degree preferred.

Key Responsibilities

• Perform ad-hoc analysis on large-scale data to extract actionable insights and support data-driven decision making.
• Develop a strong understanding of the credit risk modelling lifecycle, including data preparation, model development, validation, and monitoring.
• Build foundational knowledge of machine learning techniques and their applications in credit risk - including classification, regression, and ensemble methods.
• Support validation of existing credit risk models and assist in developing or enhancing models under the guidance of senior team members.
• Extract, manipulate, and explore large datasets using Python (pandas and related data analysis libraries) and SQL to ensure data quality and integrity.
• Communicate analytical findings and model results clearly to both technical and non-technical stakeholders, including leadership presentations

Required Skills & Experience

• Upto 4 years of experience in data science, credit risk analytics, or a related quantitative role.
• Hands-on proficiency in Python - particularly pandas, NumPy, and data analysis libraries - for data manipulation and exploratory analysis.
• Working knowledge of SQL for data extraction and querying large datasets.
• Foundational understanding of machine learning concepts and modelling techniques (e.g., logistic regression, decision trees, gradient boosting).
• Exposure to credit bureau data, credit risk concepts, or consumer lending data is a plus.
• Strong communication skills with the ability to present analysis and findings clearly to both technical teams and leadership.

Preferred / Additional Skills

• Familiarity with data visualization tools such as Power BI for presenting analytical insights.
• Basic awareness of cloud-based data platforms (AWS) and big data tools.
• Prior academic or project experience with credit scoring, risk modelling, or financial data analysis.

Educational Qualifications

• Degree from a top-tier institution; Particularly in a quantitative field (e.g., computer science, data science, engineering, economics, mathematics, etc.). Advanced degree preferred.

Key Responsibilities

• Perform ad-hoc analysis on large-scale data to extract actionable insights and support data-driven decision making.
• Develop a strong understanding of the credit risk modelling lifecycle, including data preparation, model development, validation, and monitoring.
• Build foundational knowledge of machine learning techniques and their applications in credit risk - including classification, regression, and ensemble methods.
• Support validation of existing credit risk models and assist in developing or enhancing models under the guidance of senior team members.
• Extract, manipulate, and explore large datasets using Python (pandas and related data analysis libraries) and SQL to ensure data quality and integrity.
• Communicate analytical findings and model results clearly to both technical and non-technical stakeholders, including leadership presentations

Required Skills & Experience

• Upto 4 years of experience in data science, credit risk analytics, or a related quantitative role.
• Hands-on proficiency in Python - particularly pandas, NumPy, and data analysis libraries - for data manipulation and exploratory analysis.
• Working knowledge of SQL for data extraction and querying large datasets.
• Foundational understanding of machine learning concepts and modelling techniques (e.g., logistic regression, decision trees, gradient boosting).
• Exposure to credit bureau data, credit risk concepts, or consumer lending data is a plus.
• Strong communication skills with the ability to present analysis and findings clearly to both technical teams and leadership.

Preferred / Additional Skills

• Familiarity with data visualization tools such as Power BI for presenting analytical insights.
• Basic awareness of cloud-based data platforms (AWS) and big data tools.
• Prior academic or project experience with credit scoring, risk modelling, or financial data analysis.

Educational Qualifications

• Degree from a top-tier institution; Particularly in a quantitative field (e.g., computer science, data science, engineering, economics, mathematics, etc.). Advanced degree preferred.

Key Responsibilities

• Perform ad-hoc analysis on large-scale data to extract actionable insights and support data-driven decision making.
• Develop a strong understanding of the credit risk modelling lifecycle, including data preparation, model development, validation, and monitoring.
• Build foundational knowledge of machine learning techniques and their applications in credit risk - including classification, regression, and ensemble methods.
• Support validation of existing credit risk models and assist in developing or enhancing models under the guidance of senior team members.
• Extract, manipulate, and explore large datasets using Python (pandas and related data analysis libraries) and SQL to ensure data quality and integrity.
• Communicate analytical findings and model results clearly to both technical and non-technical stakeholders, including leadership presentations

Required Skills & Experience

• Upto 4 years of experience in data science, credit risk analytics, or a related quantitative role.
• Hands-on proficiency in Python - particularly pandas, NumPy, and data analysis libraries - for data manipulation and exploratory analysis.
• Working knowledge of SQL for data extraction and querying large datasets.
• Foundational understanding of machine learning concepts and modelling techniques (e.g., logistic regression, decision trees, gradient boosting).
• Exposure to credit bureau data, credit risk concepts, or consumer lending data is a plus.
• Strong communication skills with the ability to present analysis and findings clearly to both technical teams and leadership.

Preferred / Additional Skills

• Familiarity with data visualization tools such as Power BI for presenting analytical insights.
• Basic awareness of cloud-based data platforms (AWS) and big data tools.
• Prior academic or project experience with credit scoring, risk modelling, or financial data analysis.

Educational Qualifications

• Degree from a top-tier institution; Particularly in a quantitative field (e.g., computer science, data science, engineering, economics, mathematics, etc.). Advanced degree preferred.

Key Responsibilities

• Perform ad-hoc analysis on large-scale data to extract actionable insights and support data-driven decision making.
• Develop a strong understanding of the credit risk modelling lifecycle, including data preparation, model development, validation, and monitoring.
• Build foundational knowledge of machine learning techniques and their applications in credit risk - including classification, regression, and ensemble methods.
• Support validation of existing credit risk models and assist in developing or enhancing models under the guidance of senior team members.
• Extract, manipulate, and explore large datasets using Python (pandas and related data analysis libraries) and SQL to ensure data quality and integrity.
• Communicate analytical findings and model results clearly to both technical and non-technical stakeholders, including leadership presentations

Required Skills & Experience

• Upto 4 years of experience in data science, credit risk analytics, or a related quantitative role.
• Hands-on proficiency in Python - particularly pandas, NumPy, and data analysis libraries - for data manipulation and exploratory analysis.
• Working knowledge of SQL for data extraction and querying large datasets.
• Foundational understanding of machine learning concepts and modelling techniques (e.g., logistic regression, decision trees, gradient boosting).
• Exposure to credit bureau data, credit risk concepts, or consumer lending data is a plus.
• Strong communication skills with the ability to present analysis and findings clearly to both technical teams and leadership.

Preferred / Additional Skills

• Familiarity with data visualization tools such as Power BI for presenting analytical insights.
• Basic awareness of cloud-based data platforms (AWS) and big data tools.
• Prior academic or project experience with credit scoring, risk modelling, or financial data analysis.

Educational Qualifications

• Degree from a top-tier institution; Particularly in a quantitative field (e.g., computer science, data science, engineering, economics, mathematics, etc.). Advanced degree preferred.

Key Responsibilities

• Perform ad-hoc analysis on large-scale data to extract actionable insights and support data-driven decision making.
• Develop a strong understanding of the credit risk modelling lifecycle, including data preparation, model development, validation, and monitoring.
• Build foundational knowledge of machine learning techniques and their applications in credit risk - including classification, regression, and ensemble methods.
• Support validation of existing credit risk models and assist in developing or enhancing models under the guidance of senior team members.
• Extract, manipulate, and explore large datasets using Python (pandas and related data analysis libraries) and SQL to ensure data quality and integrity.
• Communicate analytical findings and model results clearly to both technical and non-technical stakeholders, including leadership presentations

Required Skills & Experience

• Upto 4 years of experience in data science, credit risk analytics, or a related quantitative role.
• Hands-on proficiency in Python - particularly pandas, NumPy, and data analysis libraries - for data manipulation and exploratory analysis.
• Working knowledge of SQL for data extraction and querying large datasets.
• Foundational understanding of machine learning concepts and modelling techniques (e.g., logistic regression, decision trees, gradient boosting).
• Exposure to credit bureau data, credit risk concepts, or consumer lending data is a plus.
• Strong communication skills with the ability to present analysis and findings clearly to both technical teams and leadership.

Preferred / Additional Skills

• Familiarity with data visualization tools such as Power BI for presenting analytical insights.
• Basic awareness of cloud-based data platforms (AWS) and big data tools.
• Prior academic or project experience with credit scoring, risk modelling, or financial data analysis.

Educational Qualifications

• Degree from a top-tier institution; Particularly in a quantitative field (e.g., computer science, data science, engineering, economics, mathematics, etc.). Advanced degree preferred.

Key Responsibilities

• Perform ad-hoc analysis on large-scale data to extract actionable insights and support data-driven decision making.
• Develop a strong understanding of the credit risk modelling lifecycle, including data preparation, model development, validation, and monitoring.
• Build foundational knowledge of machine learning techniques and their applications in credit risk - including classification, regression, and ensemble methods.
• Support validation of existing credit risk models and assist in developing or enhancing models under the guidance of senior team members.
• Extract, manipulate, and explore large datasets using Python (pandas and related data analysis libraries) and SQL to ensure data quality and integrity.
• Communicate analytical findings and model results clearly to both technical and non-technical stakeholders, including leadership presentations

Required Skills & Experience

• Upto 4 years of experience in data science, credit risk analytics, or a related quantitative role.
• Hands-on proficiency in Python - particularly pandas, NumPy, and data analysis libraries - for data manipulation and exploratory analysis.
• Working knowledge of SQL for data extraction and querying large datasets.
• Foundational understanding of machine learning concepts and modelling techniques (e.g., logistic regression, decision trees, gradient boosting).
• Exposure to credit bureau data, credit risk concepts, or consumer lending data is a plus.
• Strong communication skills with the ability to present analysis and findings clearly to both technical teams and leadership.

Preferred / Additional Skills

• Familiarity with data visualization tools such as Power BI for presenting analytical insights.
• Basic awareness of cloud-based data platforms (AWS) and big data tools.
• Prior academic or project experience with credit scoring, risk modelling, or financial data analysis.

Educational Qualifications

• Degree from a top-tier institution; Particularly in a quantitative field (e.g., computer science, data science, engineering, economics, mathematics, etc.). Advanced degree preferred.

Key Responsibilities

• Perform ad-hoc analysis on large-scale data to extract actionable insights and support data-driven decision making.
• Develop a strong understanding of the credit risk modelling lifecycle, including data preparation, model development, validation, and monitoring.
• Build foundational knowledge of machine learning techniques and their applications in credit risk - including classification, regression, and ensemble methods.
• Support validation of existing credit risk models and assist in developing or enhancing models under the guidance of senior team members.
• Extract, manipulate, and explore large datasets using Python (pandas and related data analysis libraries) and SQL to ensure data quality and integrity.
• Communicate analytical findings and model results clearly to both technical and non-technical stakeholders, including leadership presentations

Required Skills & Experience

• Upto 4 years of experience in data science, credit risk analytics, or a related quantitative role.
• Hands-on proficiency in Python - particularly pandas, NumPy, and data analysis libraries - for data manipulation and exploratory analysis.
• Working knowledge of SQL for data extraction and querying large datasets.
• Foundational understanding of machine learning concepts and modelling techniques (e.g., logistic regression, decision trees, gradient boosting).
• Exposure to credit bureau data, credit risk concepts, or consumer lending data is a plus.
• Strong communication skills with the ability to present analysis and findings clearly to both technical teams and leadership.

Preferred / Additional Skills

• Familiarity with data visualization tools such as Power BI for presenting analytical insights.
• Basic awareness of cloud-based data platforms (AWS) and big data tools.
• Prior academic or project experience with credit scoring, risk modelling, or financial data analysis.

Educational Qualifications

• Degree from a top-tier institution; Particularly in a quantitative field (e.g., computer science, data science, engineering, economics, mathematics, etc.). Advanced degree preferred.

Key Responsibilities

• Perform ad-hoc analysis on large-scale data to extract actionable insights and support data-driven decision making.
• Develop a strong understanding of the credit risk modelling lifecycle, including data preparation, model development, validation, and monitoring.
• Build foundational knowledge of machine learning techniques and their applications in credit risk - including classification, regression, and ensemble methods.
• Support validation of existing credit risk models and assist in developing or enhancing models under the guidance of senior team members.
• Extract, manipulate, and explore large datasets using Python (pandas and related data analysis libraries) and SQL to ensure data quality and integrity.
• Communicate analytical findings and model results clearly to both technical and non-technical stakeholders, including leadership presentations

Required Skills & Experience

• Upto 4 years of experience in data science, credit risk analytics, or a related quantitative role.
• Hands-on proficiency in Python - particularly pandas, NumPy, and data analysis libraries - for data manipulation and exploratory analysis.
• Working knowledge of SQL for data extraction and querying large datasets.
• Foundational understanding of machine learning concepts and modelling techniques (e.g., logistic regression, decision trees, gradient boosting).
• Exposure to credit bureau data, credit risk concepts, or consumer lending data is a plus.
• Strong communication skills with the ability to present analysis and findings clearly to both technical teams and leadership.

Preferred / Additional Skills

• Familiarity with data visualization tools such as Power BI for presenting analytical insights.
• Basic awareness of cloud-based data platforms (AWS) and big data tools.
• Prior academic or project experience with credit scoring, risk modelling, or financial data analysis.

Educational Qualifications

• Degree from a top-tier institution; Particularly in a quantitative field (e.g., computer science, data science, engineering, economics, mathematics, etc.). Advanced degree preferred.

Key Responsibilities

• Perform ad-hoc analysis on large-scale data to extract actionable insights and support data-driven decision making.
• Develop a strong understanding of the credit risk modelling lifecycle, including data preparation, model development, validation, and monitoring.
• Build foundational knowledge of machine learning techniques and their applications in credit risk - including classification, regression, and ensemble methods.
• Support validation of existing credit risk models and assist in developing or enhancing models under the guidance of senior team members.
• Extract, manipulate, and explore large datasets using Python (pandas and related data analysis libraries) and SQL to ensure data quality and integrity.
• Communicate analytical findings and model results clearly to both technical and non-technical stakeholders, including leadership presentations

Required Skills & Experience

• Upto 4 years of experience in data science, credit risk analytics, or a related quantitative role.
• Hands-on proficiency in Python - particularly pandas, NumPy, and data analysis libraries - for data manipulation and exploratory analysis.
• Working knowledge of SQL for data extraction and querying large datasets.
• Foundational understanding of machine learning concepts and modelling techniques (e.g., logistic regression, decision trees, gradient boosting).
• Exposure to credit bureau data, credit risk concepts, or consumer lending data is a plus.
• Strong communication skills with the ability to present analysis and findings clearly to both technical teams and leadership.

Preferred / Additional Skills

• Familiarity with data visualization tools such as Power BI for presenting analytical insights.
• Basic awareness of cloud-based data platforms (AWS) and big data tools.
• Prior academic or project experience with credit scoring, risk modelling, or financial data analysis.

Educational Qualifications

• Degree from a top-tier institution; Particularly in a quantitative field (e.g., computer science, data science, engineering, economics, mathematics, etc.). Advanced degree preferred.

Key Responsibilities

• Perform ad-hoc analysis on large-scale data to extract actionable insights and support data-driven decision making.
• Develop a strong understanding of the credit risk modelling lifecycle, including data preparation, model development, validation, and monitoring.
• Build foundational knowledge of machine learning techniques and their applications in credit risk - including classification, regression, and ensemble methods.
• Support validation of existing credit risk models and assist in developing or enhancing models under the guidance of senior team members.
• Extract, manipulate, and explore large datasets using Python (pandas and related data analysis libraries) and SQL to ensure data quality and integrity.
• Communicate analytical findings and model results clearly to both technical and non-technical stakeholders, including leadership presentations

Required Skills & Experience

• Upto 4 years of experience in data science, credit risk analytics, or a related quantitative role.
• Hands-on proficiency in Python - particularly pandas, NumPy, and data analysis libraries - for data manipulation and exploratory analysis.
• Working knowledge of SQL for data extraction and querying large datasets.
• Foundational understanding of machine learning concepts and modelling techniques (e.g., logistic regression, decision trees, gradient boosting).
• Exposure to credit bureau data, credit risk concepts, or consumer lending data is a plus.
• Strong communication skills with the ability to present analysis and findings clearly to both technical teams and leadership.

Preferred / Additional Skills

• Familiarity with data visualization tools such as Power BI for presenting analytical insights.
• Basic awareness of cloud-based data platforms (AWS) and big data tools.
• Prior academic or project experience with credit scoring, risk modelling, or financial data analysis.

Educational Qualifications

• Degree from a top-tier institution; Particularly in a quantitative field (e.g., computer science, data science, engineering, economics, mathematics, etc.). Advanced degree preferred.

Key Responsibilities

• Perform ad-hoc analysis on large-scale data to extract actionable insights and support data-driven decision making.
• Develop a strong understanding of the credit risk modelling lifecycle, including data preparation, model development, validation, and monitoring.
• Build foundational knowledge of machine learning techniques and their applications in credit risk - including classification, regression, and ensemble methods.
• Support validation of existing credit risk models and assist in developing or enhancing models under the guidance of senior team members.
• Extract, manipulate, and explore large datasets using Python (pandas and related data analysis libraries) and SQL to ensure data quality and integrity.
• Communicate analytical findings and model results clearly to both technical and non-technical stakeholders, including leadership presentations

Required Skills & Experience

• Upto 4 years of experience in data science, credit risk analytics, or a related quantitative role.
• Hands-on proficiency in Python - particularly pandas, NumPy, and data analysis libraries - for data manipulation and exploratory analysis.
• Working knowledge of SQL for data extraction and querying large datasets.
• Foundational understanding of machine learning concepts and modelling techniques (e.g., logistic regression, decision trees, gradient boosting).
• Exposure to credit bureau data, credit risk concepts, or consumer lending data is a plus.
• Strong communication skills with the ability to present analysis and findings clearly to both technical teams and leadership.

Preferred / Additional Skills

• Familiarity with data visualization tools such as Power BI for presenting analytical insights.
• Basic awareness of cloud-based data platforms (AWS) and big data tools.
• Prior academic or project experience with credit scoring, risk modelling, or financial data analysis.

Educational Qualifications

• Degree from a top-tier institution; Particularly in a quantitative field (e.g., computer science, data science, engineering, economics, mathematics, etc.). Advanced degree preferred.

Key Responsibilities

• Perform ad-hoc analysis on large-scale data to extract actionable insights and support data-driven decision making.
• Develop a strong understanding of the credit risk modelling lifecycle, including data preparation, model development, validation, and monitoring.
• Build foundational knowledge of machine learning techniques and their applications in credit risk - including classification, regression, and ensemble methods.
• Support validation of existing credit risk models and assist in developing or enhancing models under the guidance of senior team members.
• Extract, manipulate, and explore large datasets using Python (pandas and related data analysis libraries) and SQL to ensure data quality and integrity.
• Communicate analytical findings and model results clearly to both technical and non-technical stakeholders, including leadership presentations

Required Skills & Experience

• Upto 4 years of experience in data science, credit risk analytics, or a related quantitative role.
• Hands-on proficiency in Python - particularly pandas, NumPy, and data analysis libraries - for data manipulation and exploratory analysis.
• Working knowledge of SQL for data extraction and querying large datasets.
• Foundational understanding of machine learning concepts and modelling techniques (e.g., logistic regression, decision trees, gradient boosting).
• Exposure to credit bureau data, credit risk concepts, or consumer lending data is a plus.
• Strong communication skills with the ability to present analysis and findings clearly to both technical teams and leadership.

Preferred / Additional Skills

• Familiarity with data visualization tools such as Power BI for presenting analytical insights.
• Basic awareness of cloud-based data platforms (AWS) and big data tools.
• Prior academic or project experience with credit scoring, risk modelling, or financial data analysis.

Educational Qualifications

• Degree from a top-tier institution; Particularly in a quantitative field (e.g., computer science, data science, engineering, economics, mathematics, etc.). Advanced degree preferred.

Key Responsibilities

• Perform ad-hoc analysis on large-scale data to extract actionable insights and support data-driven decision making.
• Develop a strong understanding of the credit risk modelling lifecycle, including data preparation, model development, validation, and monitoring.
• Build foundational knowledge of machine learning techniques and their applications in credit risk - including classification, regression, and ensemble methods.
• Support validation of existing credit risk models and assist in developing or enhancing models under the guidance of senior team members.
• Extract, manipulate, and explore large datasets using Python (pandas and related data analysis libraries) and SQL to ensure data quality and integrity.
• Communicate analytical findings and model results clearly to both technical and non-technical stakeholders, including leadership presentations

Required Skills & Experience

• Upto 4 years of experience in data science, credit risk analytics, or a related quantitative role.
• Hands-on proficiency in Python - particularly pandas, NumPy, and data analysis libraries - for data manipulation and exploratory analysis.
• Working knowledge of SQL for data extraction and querying large datasets.
• Foundational understanding of machine learning concepts and modelling techniques (e.g., logistic regression, decision trees, gradient boosting).
• Exposure to credit bureau data, credit risk concepts, or consumer lending data is a plus.
• Strong communication skills with the ability to present analysis and findings clearly to both technical teams and leadership.

Preferred / Additional Skills

• Familiarity with data visualization tools such as Power BI for presenting analytical insights.
• Basic awareness of cloud-based data platforms (AWS) and big data tools.
• Prior academic or project experience with credit scoring, risk modelling, or financial data analysis.

Educational Qualifications

• Degree from a top-tier institution; Particularly in a quantitative field (e.g., computer science, data science, engineering, economics, mathematics, etc.). Advanced degree preferred.

Key Responsibilities

• Perform ad-hoc analysis on large-scale data to extract actionable insights and support data-driven decision making.
• Develop a strong understanding of the credit risk modelling lifecycle, including data preparation, model development, validation, and monitoring.
• Build foundational knowledge of machine learning techniques and their applications in credit risk - including classification, regression, and ensemble methods.
• Support validation of existing credit risk models and assist in developing or enhancing models under the guidance of senior team members.
• Extract, manipulate, and explore large datasets using Python (pandas and related data analysis libraries) and SQL to ensure data quality and integrity.
• Communicate analytical findings and model results clearly to both technical and non-technical stakeholders, including leadership presentations

Required Skills & Experience

• Upto 4 years of experience in data science, credit risk analytics, or a related quantitative role.
• Hands-on proficiency in Python - particularly pandas, NumPy, and data analysis libraries - for data manipulation and exploratory analysis.
• Working knowledge of SQL for data extraction and querying large datasets.
• Foundational understanding of machine learning concepts and modelling techniques (e.g., logistic regression, decision trees, gradient boosting).
• Exposure to credit bureau data, credit risk concepts, or consumer lending data is a plus.
• Strong communication skills with the ability to present analysis and findings clearly to both technical teams and leadership.

Preferred / Additional Skills

• Familiarity with data visualization tools such as Power BI for presenting analytical insights.
• Basic awareness of cloud-based data platforms (AWS) and big data tools.
• Prior academic or project experience with credit scoring, risk modelling, or financial data analysis.

Educational Qualifications

• Degree from a top-tier institution; Particularly in a quantitative field (e.g., computer science, data science, engineering, economics, mathematics, etc.). Advanced degree preferred.

Key Responsibilities

• Perform ad-hoc analysis on large-scale data to extract actionable insights and support data-driven decision making.
• Develop a strong understanding of the credit risk modelling lifecycle, including data preparation, model development, validation, and monitoring.
• Build foundational knowledge of machine learning techniques and their applications in credit risk - including classification, regression, and ensemble methods.
• Support validation of existing credit risk models and assist in developing or enhancing models under the guidance of senior team members.
• Extract, manipulate, and explore large datasets using Python (pandas and related data analysis libraries) and SQL to ensure data quality and integrity.
• Communicate analytical findings and model results clearly to both technical and non-technical stakeholders, including leadership presentations

Required Skills & Experience

• Upto 4 years of experience in data science, credit risk analytics, or a related quantitative role.
• Hands-on proficiency in Python - particularly pandas, NumPy, and data analysis libraries - for data manipulation and exploratory analysis.
• Working knowledge of SQL for data extraction and querying large datasets.
• Foundational understanding of machine learning concepts and modelling techniques (e.g., logistic regression, decision trees, gradient boosting).
• Exposure to credit bureau data, credit risk concepts, or consumer lending data is a plus.
• Strong communication skills with the ability to present analysis and findings clearly to both technical teams and leadership.

Preferred / Additional Skills

• Familiarity with data visualization tools such as Power BI for presenting analytical insights.
• Basic awareness of cloud-based data platforms (AWS) and big data tools.
• Prior academic or project experience with credit scoring, risk modelling, or financial data analysis.

Educational Qualifications

• Degree from a top-tier institution; Particularly in a quantitative field (e.g., computer science, data science, engineering, economics, mathematics, etc.). Advanced degree preferred.

Key Responsibilities

• Perform ad-hoc analysis on large-scale data to extract actionable insights and support data-driven decision making.
• Develop a strong understanding of the credit risk modelling lifecycle, including data preparation, model development, validation, and monitoring.
• Build foundational knowledge of machine learning techniques and their applications in credit risk - including classification, regression, and ensemble methods.
• Support validation of existing credit risk models and assist in developing or enhancing models under the guidance of senior team members.
• Extract, manipulate, and explore large datasets using Python (pandas and related data analysis libraries) and SQL to ensure data quality and integrity.
• Communicate analytical findings and model results clearly to both technical and non-technical stakeholders, including leadership presentations

Required Skills & Experience

• Upto 4 years of experience in data science, credit risk analytics, or a related quantitative role.
• Hands-on proficiency in Python - particularly pandas, NumPy, and data analysis libraries - for data manipulation and exploratory analysis.
• Working knowledge of SQL for data extraction and querying large datasets.
• Foundational understanding of machine learning concepts and modelling techniques (e.g., logistic regression, decision trees, gradient boosting).
• Exposure to credit bureau data, credit risk concepts, or consumer lending data is a plus.
• Strong communication skills with the ability to present analysis and findings clearly to both technical teams and leadership.

Preferred / Additional Skills

• Familiarity with data visualization tools such as Power BI for presenting analytical insights.
• Basic awareness of cloud-based data platforms (AWS) and big data tools.
• Prior academic or project experience with credit scoring, risk modelling, or financial data analysis.

Educational Qualifications

• Degree from a top-tier institution; Particularly in a quantitative field (e.g., computer science, data science, engineering, economics, mathematics, etc.). Advanced degree preferred.

Objective : We are planning to hire a Data Science & Analytics professional. The incumbent will be responsible for setting up data analytics practice aligned towards organisation goals.

Roles & Responsibilities:

• Building cutting edge data analytics.
• Collaborate closely with internal stakeholders.
• Participating in the development of operating policies and procedures to promote operational excellence across the data science and analytics function .

Knowledge and experience:

• Preferred Masters degree in Statistics / Applied Mathematics and Computer Science.
• Extensive Experience in Python and SQL.
• 8 to 10 Years of relevant experience.

Role: Engineering Manager, Data Science and Machine Learning

Location: Vashi

Shift: General

Role Overview

As an Engineering Manager, AI & ML (Data Collection), you will play a vital role in executing the company’s AI and machine learning initiatives, with a strong focus on document ingestion and enrichment technologies. This position requires deep technical expertise in unstructured data processing, data collection pipeline engineering, and a hands-on approach to managing and mentoring engineers.

Your leadership will ensure that AI & ML data collection systems are developed and operationalized to the highest standards of performance, reliability, and security. You will work closely with individual contributors to ensure projects align with broader business goals and AI/ML strategies.

This role requires deep engagement in the design, development, and maintenance of document enrichment AI & ML models, solutions, architecture, and services. You will provide strong technical direction, solve complex technical challenges, and ensure the team consistently delivers high-quality, scalable solutions. You will leverage your deep knowledge in document understanding and enrichment, advanced natural language processing (NLP), OCR, entity extraction and enrichment, duplicate detection, generative AI (GenAI), large language models (LLMs), ML Operations (MLOps), data architec

Dun & Bradstreet Technology and Corporate Services India LLP is looking for candidates to support the Data Science team in Trade and Credit Risk Analytics. The candidate needs to work closely with the team based in India and across a range of Analytics leaders who are located globally to fulfill delivery on a timely basis

Key Responsibilities:

• Work on development of B2B Risk solutions which includes Standard and custom solutions catering to various clients including fortune 500 companies
• Work with internal / external D&B clients and stakeholders; Participate in all aspects of a modelling engagement, including design, development, validation, calibration, documentation, approval, implementation, monitoring, and reporting
• Ability to applying LLMs, and prompt engineering to analyze large-scale, unstructured and structured B2B datasets (e.g., Company News, Corporate Annual Reports) for credit risk, fraud detection, and compliance.
• Design, develop and test new risk signals to effectively identify risk patterns from structured and Unstructured data
• Serve as a Subject Matter Expert on risk models within the Analytics team and with business users; consult with the business, as appropriate, on predictive modelling solutions
• Develop AI Agents for business risk monitoring, deploying autonomous agents. These agents utilize Machine Learning (ML) and Natural Language Processing (NLP) to de

Key Responsibilities

• Perform ad-hoc analysis on large-scale data to extract actionable insights and support data-driven decision making.
• Develop a strong understanding of the credit risk modelling lifecycle, including data preparation, model development, validation, and monitoring.
• Build foundational knowledge of machine learning techniques and their applications in credit risk — including classification, regression, and ensemble methods.
• Support validation of existing credit risk models and assist in developing or enhancing models under the guidance of senior team members.
• Extract, manipulate, and explore large datasets using Python (pandas and related data analysis libraries) and SQL to ensure data quality and integrity.
• Communicate analytical findings and model results clearly to both technical and non-technical stakeholders, including leadership presentations

Required Skills & Experience

• Upto 4 years of experience in data science, credit risk analytics, or a related quantitative role.
• Hands-on proficiency in Python — particularly pandas, NumPy, and data analysis libraries — for data manipulation and exploratory analysis.
• Working knowledge of SQL for data extraction and querying large datasets.
• Foundational understanding of machine learning concepts and modelling techniques (e.g., logistic regression, decision trees, gradient boos

Company Description

Chandigarh University is ranked 19th in NIRF among India's top universities and is recognized as the No. 1 private university in Asia by QS Asia Rankings. Known for academic excellence and research, the university provides a world-class learning environment and state-of-the-art facilities. Chandigarh University is committed to fostering innovation and preparing students to be leaders in their fields.

Role Description

This is a full-time, on-site role based in Mohali district for an Assistant Professor of Data Science with a specialization in Artificial Intelligence. The Assistant Professor will be responsible for delivering lectures, designing and updating course curricula, conducting research, mentoring students, and contributing to academic development. Additional responsibilities include supervising student projects, publishing in reputed journals, and engaging in departmental and institutional activities.

Qualifications

• In-depth knowledge of Data Science, Machine Learning, and Artificial Intelligence
• Strong skills in programming languages such as Power BI, SQL, Data Modelling, AIML, Python, R, or MATLAB
• Experience in data visualization and data analysis tools
• Familiarity with neural networks, natural language processing, or computer vision
• Proficiency in research methodologies and academic publishing
• Capability to design

Minimum years of Experience - 15 years

Collinson

Collinson is the global, privately-owned company dedicated to helping the world to travel with ease

and confidence.

We use our expertise and products to craft customer experiences which enable some of the world’s

best known brands to acquire, engage and retain the most demanding and choice-rich customers. In

particular, our unique expertise and insight into high earning, frequent travellers allows us to create

products and solutions for our clients that inspire greater customer engagement to drive more

profitable relationships, enrich their travel experiences, protect what matters and assist in in times of

need.

While specialising in Financial Services, Travel and Retail, we also support clients in multiple sectors.

We have worked with over 90 airlines, 20 hotel groups and more than 600 financial institutions and

banks, with clients including Accor Hotels, Air France KLM, Amex, British Airways,

Cathay Pacific, Diners Club, Mandarin Oriental, Mastercard, Radisson Hotel Group, Sephora, Visa

and Vhi.

We take our 30 years’ experience working with these kinds of household names in over 170

countries, and help our clients to deliver the smarter experiences it takes to differentiate their

propositions, and help them win deeper devotion w

About Firstsource

Firstsource Solutions Limited, an RP-Sanjiv Goenka Group company (NSE: FSL, BSE: 532809, Reuters: FISO.BO, Bloomberg: FSOL:IN), is a specialized global business process services partner, providing transformational solutions and services spanning the customer lifecycle across Healthcare, Banking and Financial Services, Communications, Media and Technology, Retail, and other diverse industries. With an established presence in the US, the UK, India, Mexico, Australia, South Africa, and the Philippines, we make it happen for our clients, solving their biggest challenges with hyper-focused, domain-centered teams and cutting-edge tech, data, and analytics. Our real-world practitioners work collaboratively to deliver future-focused outcomes.

Director – Data Science

Location: India

Experience: 15+ Years

Shape the Future of AI at Firstsource

At Firstsource Solutions Ltd, we’re on a mission to transform businesses through the power of data, advanced analytics, and AI. We’re looking for a Director – Data Science to lead this charge—someone who thrives on solving complex problems, inspiring teams, and driving innovation that makes a real difference.

This is your opportunity to lead a high-performing team, partner with global clients, and design scalable AI solutions that impact millions of lives.

What You’ll Do

• Lead with Vision: Define and e

Sigmoid enables business transformation using data and analytics, leveraging real-time insights to make accurate and fast business decisions, by building modern data architectures using cloud and open source. Some of the world’s largest data producers engage with Sigmoid to solve complex business problems. Sigmoid brings deep expertise in data engineering, predictive analytics, artificial intelligence, and DataOps.

Sigmoid has been recognized as one of the fastest growing technology companies in North America, 2021, by Financial Times, Inc. 5000, and Deloitte Technology Fast 500.

Offices: New York | Dallas | San Francisco | Lima | Bengaluru

The below role is for our Bengaluru office.

Why Join Sigmoid?

● Sigmoid provides the opportunity to push the boundaries of what is possible by seamlessly combining technical expertise and creativity to tackle intrinsically complex business problems and convert them into straight-forward data solutions.

● Despite being continuously challenged, you are not alone. You will be part of a fast-paced diverse environment as a member of a high-performing team that works together to energize and inspire each other by challenging the status quo

● Vibrant inclusive culture of mutual respect and fun through both work and play

Roles and Responsibilities:

● This is a leadership position in the data science group

Sasi Creative School of Architecture and Design (SCSAD) is expanding its interdisciplinary B.Des program with a new program in B.Design - Data Visualisation and Data Science where students build data stories, dashboards, and interactive visualizations. We are seeking faculty members to join our top-tier design institution in Coimbatore, Tamil Nadu.

Minimum Requirements:

• Education: B.Des / B.Tech / B.E (Degree specializing in Data Science or Data Visualization preferred).
• Experience: 3+ years of professional experience.
• Core Skills: Mandatory knowledge of Data Science; ability to teach through projects, labs, and portfolio mentoring.

Role Specializations:

• Studio: Data Visualization & Information Design (storytelling, visual systems).
• Applied: Data Science / ML (Python analytics, modeling, evaluation).
• Engineering: Interactive visualizations, dashboards, web implementations.
• Ethics: Statistics, research methods, and responsible storytelling bias.

Technical Stack (Expected):

• Programming: Python (Pandas, NumPy, Matplotlib), SQL, Jupyter/Colab.
• Visualization/BI: Tableau, Power BI, Flourish, Datawrapper.
• Design: Figma, Adobe Illustrator (After Effects is a plus).
• Web (Plus): D3.js, HTML/CSS/JavaScr

The Assistant Manager - Data Science is responsible for developing, deploying and

operationalizing data science and machine learning solutions that support AI, analytics and

automation use cases. The role works with Solution Architects, Data Engineers and

business teams to translate business problems into analytical models and insights,

leveraging the data architecture defined by the Architecture Lead and Solution Architect.

The position ensures robust, accurate and scalable models for use cases such as

Manufacturing Control Tower, Golden Batch, Yield Improvement, OOS prediction and

pricing models.

1. Roles Played

• Assistant Manager - Data Science for AI and analytics initiatives.

• Owner of data science models, analytical workflows and model performance SLAs.

• Key contributor to feature engineering, model quality and responsible AI / governance practices.

2. Key Platforms / Technologies

• Azure Machine Learning, Databricks, Python, LLM/GenAI frameworks and ML model development platforms.

• Statistical analysis, machine learning, deep learning and LLM orchestration pipelines.

• Batch and real-time analytics / scoring frameworks.

• BI environments (Power BI, Tableau) for insight consumption and model output visualization.

• Integration with SAP ECC or S/4 HANA, MES, LIMS, Empower, C

Technical Content Developer

About NxtWave

NxtWave was founded by Rahul Attuluri (ex-Amazon, IIIT Hyderabad), Sashank Reddy (IIT Bombay), and Anupam Pedarla (IIT Kharagpur). In February 2023, NxtWave raised ₹275 crore, led by Greater Pacific Capital, a leading international private equity firm . The startup is also backed by Orios Ventures, Better Capital, and marquee angels, including the founders of some of India’s unicorns.

NxtWave is one of India’s fastest-growing Ed-Tech startups , revolutionising the 21st-century job market by transforming youth into highly skilled tech professionals irrespective of their educational background with its CCBP 4.0 programs.

As an official partner for NSDC under the Ministry of Skill Development & Entrepreneurship, Government of India, and recognised by NASSCOM, the Ministry of Commerce and Industry, Government of India, and Startup India, NxtWave has earned a reputation for excellence. The startup has received accolades such as ‘The Greatest Brand in Education’ in a research-based listing by URS Media, a leading international media house. NxtWave has also been honoured with the "Startup Spotlight Award 2023" by T-Hub on its 8th Foundation Day.

Recently, NxtWave’s founders, Anupam Pedarla and Sashank Gujjula, were honoured in the 2024 Forb

Hiring | Principal Data Scientist (IC Leadership Role)

We are looking for a Principal Data Scientist to drive high-impact AI/ML initiatives, shape data strategy, and solve complex business problems at scale.

Location: Mumbai (Work from Office)

Experience: 10+ Years

Key Responsibilities

• Lead advanced ML, statistical & Agentic AI solutions

• Own end-to-end model lifecycle (design → deployment)

• Drive experimentation, causal inference & decision science

• Partner with business & product leaders on data strategy

• Mentor teams & define best practices and standards

Must Have

• Strong expertise in ML, Statistics & Python/SQL

• Experience with production-grade ML systems

• Proven track record of solving complex, high-impact problems

➕ Good to Have

• Cloud (AWS/GCP/Azure), MLOps , Spark/BigQuery/Snowflake

• Domain expertise in NLP, forecasting, personalization, risk

Ideal for senior ICs who want to drive technical leadership without people management .

Interested or referrals? Share CV at: gautam@moun

Job Title: Data Science Trainer

Organization: SkillCircle

Location: CP , Pitampura

Employment Type: Full-time

Job Summary:

SkillCircle is looking for a passionate and knowledgeable Data Science Trainer to

mentor and guide students through their journey into the world of data science.

The ideal candidate should have a strong foundation in Python, machine learning,

data analytics, and the ability to simplify complex concepts for learners at various

levels.

Required Skills:

 Proficiency in Python, Pandas, NumPy, Scikit-learn, Matplotlib/Seaborn,

Tableau and Power BI.

 Knowledge of Machine Learning algorithms and their real-world

applications.

 Understanding of statistics, data preprocessing, and EDA.

 Excellent communication and presentation skills.

 Ability to engage and inspire students from non-technical backgrounds.

Eligibility Criteria:

 Bachelor’s degree in Computer Science, Data Science, Engineering, or a

related field.

 2–5 years of experience in teaching/training or industry experience in data

science roles.

 Prior experience as a trainer or mentor is a plus.

 Certifications in Data Science or Machine Learning (preferred but not

mandatory).

Perks and Benefi

Job Title: Python Tutor/Trainer (2 Years Experience)

Location: Noida Sector 63

Interview Availability : Face to Face interview only

Working Days: 6 Days a Week

Job Type: Full-Time

Job Summary:

We are seeking a passionate and experienced Python Tutor/Trainer with a minimum of 2 years of hands-on teaching/training experience. The ideal candidate will have a strong foundation in Python programming and a proven track record of training students or professionals effectively. This role involves delivering classroom and/or online sessions, creating course content, and mentoring learners.

Key Responsibilities:

Deliver comprehensive training in Python to students, professionals, or corporate clients.

Develop and update course materials, assignments, and assessments as per industry standards.

Explain complex concepts in a clear and engaging manner.

Provide hands-on coding sessions, real-time project guidance, and doubt-clearing sessions.

Monitor student progress and provide regular feedback.

Keep up to date with the latest Python technologies, tools, and trends.

Conduct tests and evaluations to assess learner understanding and knowledge retention.

Support curriculum development and course design improvements.

Requirements:

Minimum 2 years of experience as a Python and AI Trainer or in a similar training/te

About Us : Kauvery Group of Hospitals launched its first hospital more than two decades ago. The founders of Kauvery Hospital were determined on creating world-class healthcare facilities that shall be affordable. The founding doctors set off on this dream in 1999 with a 30-bedded hospital in Trichy, focus on offering the best-in-class healthcare, with a personal touch. This was a very new concept in a tier 2 city like Trichy which lacked a tertiary care hospital at the time. Today, Kauvery is a multi-specialty hospital chain with 2250+ beds in six locations including Trichy, Chennai, Salem, Hosur, Tirunelveli and Bengaluru. With twelve hospitals and a workforce of over 8000+, Kauvery’s mission is to provide exemplary secondary and tertiary care

Our Vision

To be the most respected and trusted healthcare provider

Our Mission

To make great healthcare affordable

Our Values

• Continual Improvement
• Heartfelt Personal Touch
• Ethical
• Empathetic Care
• Real Accountability
• Service Excellence

Job Title: Data Science Technologist (AI Integration)

Department: Information Technology

Location: Kauvery Hospital, Trichy!

Reporting To: In-charge

Qualification: BSC Computer Science / BCA /MCA / M.Sc. Computer Science / Data Science

Pricing and Data Science Specialist

Your Job:

You are looking for a career opportunity where you can contribute both with your strong analytical expertise and with your interest for developing and growing important business processes? You would like to continuously develop and optimize existing processes but also take advantage of personal development opportunities, e.g., by taking over exciting global projects?

Then our position as Pricing and Data Science Specialist might just be the ideal fit for you. You will be part of the global Business Excellence Team within the Sulzer Services Division and work for the global parts pricing team: In this wide-spread role you are supporting the automation and improvement of the data-driven global price management.

This includes:

Pricing

• Optimization of local and global pricing strategies for the global Sulzer after-markets
• Preparation of profitability studies and deduction of financial effects of price adjustments
• Development, implementation, and improvement of global pricing tools for internal customers
• Supporting the automation of price setting and price calculations
• Quantitative data analyses for a harmonized price structure

Data Science

• In addition to pricing, apply your data science capabilities in strategic global projects in contexts of market, sales and cost analytics <

Job Title: Data Science Trainer

Company: WsCube Tech

Location: Jaipur, Rajasthan

Role Type: Full-Time

Experience Required: 2+ Years (Industry or Training)

About WsCube Tech:

WsCube Tech is a Vernacular Upskilling EdTech company dedicated to creating job-ready professionals for the global workforce. Our mission is to bridge skill gaps with tech-powered career acceleration programs and impact over 100 million career aspirants in the next five years.

You can learn more about us via our website at 

and YouTube channel at 

About the Role:

WsCube Tech is looking for an experienced Data Science Trainer who can deliver high-quality training on Data Analytics, Machine Learning, and industry tools. The trainer should be comfortable teaching both theory and practical concepts and guiding learners toward industry readiness.

Key Responsibilities:

1. Deliver training sessions on Python, SQL, Machine Learning, Statistics, and Power BI.
2. Conduct classroom training, doubt-solving, and one-on-one learner mentorship.
3. Prepare and update course material, assignments, assessments, and real-world projects.
4. Evaluate learner performance and share structured feedback.
5. Guide learners on project building, interview

1. POSITION VACANT: Senior Research Advisor – Data Science, Vital Strategies India Services Private Limited (VSISPL), New Delhi

2. ORGANISATIONAL AND PROJECT BACKGROUND:

Vital Strategies India Services Private Limited , with a registered office at New Delhi, is a social enterprise that provides technical and management consultancy services to address the leading causes of death, disease or injury and improve health outcomes. It seeks to provide innovative, cost-efficient solutions to global clients that are locally relevant, effective, and enduring by pursuing high-value partnerships and leveraging India’s high-quality human capital and technology advantage.

All members of the Vital Strategies India team embody the organization's values and are integral to promoting our brand, supporting our growth, and ensuring operational excellence.

For more information about Vital Strategies , visit

3. JOB DESCRIPTION/ RESPONSIBILITIES:

Reporting to the Associate Director – Research , the Senior Research Advisor – Data Science (SRA-DS) based in New Delhi, will lead the design, management, and translation of advanced data science and digital intelligence initiatives within the Policy & Communication Division of Vital Strategies India Services Private Limited .

Job Title: Director — Data Scientist

Location: Bengaluru

Experience: 14+ Years

Responsibilities:

• Build end to end DS/AI/ML solutions.
• Own analyses, model training, deployment, business impact A/B evaluation, and subsequent iterations, for best-in-class data science models.
• Primary focus on dynamic pricing and discounting modules for various lines of business (flights, hotels, domestic, international, B2C meta) and potentially other products of both brands MakeMyTrip and GoIbibo.
• Work with stakeholders at various stages of the project, lead project development.
• Conduct iterative experiments, demonstrating incremental lift in business metrics due to AI/ML initiatives.
• Improve the system quality with best-in-class models and engineering tech.
• Opportunity to work with large click stream e-commerce data sets, at MakeMyTrip, GoIbibo, and TripMoney FinTech.
• Build and own robust data science APIs meeting 99% SLA at very high RPS. Also, ensure training/inference MLOps rigour.
• Show business impact while having the opportunity to build best-in-class AI/ML models.

Requirements:

• Bachelor's degree in CS, EE, Mathematics, Statistics, Engineering technical field, or equivalent practical experience.
• A minimum of 14-18 years of experience (minimum of 10 years with a Ph. D. ) in applied DS /AI/ ML.
• Pro

Job Title: Instructor Offline Learning Centre

Location: Panipat, Haryana

Working Days: 6 (Monday to Saturday)

About upGrad

Founded in 2015, upGrad is Asias largest integrated Learning, Skilling, Workforce Development, and Placement company. Its offerings include online and hybrid degrees, study abroad programs,

certifications, bootcamps, and doctorates for working professionals. Through its B2B arm, upGrad

also provides enterprise training, recruitment, and staffing services. With 10M+ learners across 100+countries, 300+ global university partners, 2500+ pathway connections, and 20,000+ recruitment partners, upGrad continues to empower careers worldwide.

Learn more:

About the Role

As an Instructor at our Offline Learning Centres, you will deliver engaging sessions in Data Science, Machine Learning, Deep Learning, and related fields to students and working professionals. You will play a vital role in ensuring high-quality learning outcomes through classroom teaching, mentorship, and academic support.

This is a unique opportunity to combine your passion for technology and education, while shaping the careers of tomorrow.

6 Reasons to Join upGrad Offline Learning Centres

1. Career Growth & SME Pathway: Accelerate your career with opportunities to move into leadership roles and grow as a Subject Matter Expert by contributi

Job Summary

We are seeking a seasoned Data Science Delivery Leader to lead the end-to-end delivery of large, complex Data Science, Advanced Analytics, AI, GenAI, and Agentic AI–driven programs across multiple business verticals. This role is part of Data Science Practice and works horizontally with vertical and functional teams to ensure consistent, high-quality delivery outcomes.

The ideal candidate will bring deep technical expertise in Data Science, Machine Learning, Generative AI, and Agentic AI systems, strong program and project management skills, and proven experience managing large delivery teams and enterprise-scale programs. This is a pure delivery leadership role with no P&L, capability building, or practice management responsibilities.

Key Responsibilities

1. Delivery & Technical Oversight

• Own and drive end-to-end delivery of large-scale, multi-year Data Science, AI, GenAI, and Agentic AI programs, ensuring adherence to scope, timelines, quality, and cost.

• Provide strong technical oversight across solution design, ML/AI model development, GenAI solution architecture (LLMs, RAG, fine-tuning), agent workflows, validation, deployment, and productionization.

• Ensure delivery aligns with agreed enterprise architectures, data standards, security, Responsible AI, and governance requirements.

• Act as the senior escalation point for critical technical, architectural, and delivery

About the Opportunity

Role: Director - Data Science (Data Platforms & Personalization)

Reporting to: Vice President - Technology

Location: Bangalore

About the Function

The Data Platforms and Personalization team at MakeMyTrip is at the forefront of transforming the traveler experience by leveraging data and intelligence to deliver highly relevant, seamless, and engaging journeys. The team focuses on building scalable data platforms and advanced personalization capabilities that enable tailored recommendations, contextual interactions, and optimized user journeys across the platform. Join us to be part of this revolution and drive platform driven personalization to the next level!

About the Role

As a Director - Data Science in data platforms and personalization team, the role focuses on building AI-driven travel experiences by designing intelligent agents for trip planning, itinerary creation, and real-time user support. It involves developing personalized and dynamic recommendation systems, as well as creating multi-modal solutions that leverage text, images, and user preferences to enhance the overall travel experience.

Key Responsibilities

• Design and develop AI agents that assist travelers in trip planning, itinerary creation, and r

Job Overview:

We are seeking a highly motivated Admission Counselor with proven experience in selling software courses, specifically in domains like AI/ML and Data Science. The candidate will be responsible for guiding prospective learners and driving enrollments through effective consultative selling.

Key Responsibilities:

• Counsel prospective students on AI/ML, Data Science, and other software programs
• Understand learner needs and recommend suitable courses based on career goals
• Handle inbound and outbound leads via calls, emails, and virtual sessions
• Drive conversions by delivering compelling course pitches and resolving objections
• Consistently achieve monthly admission and revenue targets
• Maintain accurate lead data and follow-ups in CRM systems
• Stay updated on industry trends in AI, Machine Learning, and Data Science
• Coordinate with marketing and academic teams to improve conversion rates

Required Skills & Qualifications:

• 1–2 years of experience in selling software/IT courses (mandatory)
• Prior experience selling AI/ML or Data Science programs is highly preferred
• Strong sales, negotiation, and closing skills
• Excellent communication and interpersonal skills
• Ability to explain technical concepts in a simple and engaging manner
• Target-driven mindset with a proven sales track recor