Find Your Dream Data Science Job in India

About the Role

We are looking for an experienced ML & Data Science Architect to lead the design and evolution of Bosenet's machine learning and data science ecosystem.

This role is focused on platform architecture, model governance, MLOps maturity, and scalable ML infrastructure. You will define the standards, tooling, and operational frameworks that enable ML Engineers and Data Scientists to build production-ready machine learning systems efficiently and responsibly.

You will work closely with AI Architects, Engineering Leaders, Product Teams, and Business Stakeholders to ensure ML investments align with measurable business outcomes.

Key Responsibilities

ML Platform Architecture

• Design and own scalable ML platform architecture.
• Define standards for feature stores, model registries, experiment tracking, and serving infrastructure.
• Build frameworks for reproducible and production-ready ML systems.

MLOps & Automation

• Establish enterprise-grade MLOps processes.
• Design automated retraining pipelines, monitoring systems, rollback mechanisms, and deployment workflows.
• Implement drift detection, model monitoring, and ML observability.

Data Architecture & Governance

• Design scalable data platforms including lakehouses, feature engineering pipelines, and lineage tracking.
• Define data governance, quality, and compliance standards.
• Ensure reproducibility and auditability across ML workflows.

Model Governance & Responsible AI

• Define standards for model explainability, fairness, bias detection, and evaluation.
• Create governance policies for production model approvals.
• Standardize ML documentation and model lifecycle management.

Leadership & Collaboration

• Lead architecture reviews for ML systems and data platforms.
• Mentor ML Engineers and Data Scientists.
• Partner with engineering and business teams to align ML strategy with organisational goals.
• Present ML performance and platform strategy to executive stakeholders.

Required Skills & Experience

• 5-8+ years of experience in Machine Learning or Data Science with at least 2 years in an Architect, Platform Lead, or Principal Engineer role.
• Strong experience designing enterprise ML platforms and MLOps ecosystems.
• Hands-on expertise with:
• MLflow
• Kubeflow
• ZenML
• Airflow
• Feature Stores
• Experiment Tracking
• Strong knowledge of ML frameworks:
• PyTorch
• TensorFlow
• Scikit-learn
• Experience with data platforms and modern data architecture:
• Delta Lake
• Apache Iceberg
• ELT Pipelines
• Data Warehouses
• Hands-on experience with cloud ML services:
• AWS SageMaker
• Azure ML
• GCP Vertex AI
• Strong understanding of statistical modelling, evaluation metrics, and responsible AI practices.
• Proficiency in Python, SQL, Spark, and data engineering tooling.
• Strong communication and stakeholder management capabilities.

Nice-to-Have Skills

• Cloud ML or Data Engineering Certifications.
• Experience with real-time ML inference systems.
• Experience with GenAI infrastructure and embedding pipelines.
• Exposure to financial forecasting, OCR systems, NLP, or document intelligence.
• Background in research-driven or highly regulated environments.

Core Technology Stack

• Python / SQL / PySpark
• PyTorch / TensorFlow
• MLflow / Weights & Biases / DVC
• Kubeflow / Airflow / ZenML
• Feast / Tecton
• Delta Lake / Apache Iceberg
• SageMaker / Vertex AI / Azure ML
• Docker / Kubernetes
• dbt / ELT Pipelines
• SHAP / LIME
• CI/CD for ML
• Real-Time Inference Systems
• Responsible AI Frameworks

About the Company

Dhurin is a fast-growing data science firm serving financial services industry. We work with banks, NBFCs, fintech and other financial institutions. Our work is in the space of risk, fraud, collections, marketing, sales, and operations through it we directly influence how financial institutions make smarter decisions. Dhurin is 'the place' for someone who is passionate about data and wants to grow with a well-positioned firm.

Role Overview

As a Data Science Manager, you will lead and deliver high-impact AI/ML engagements across domains, driving the design, development, and deployment of scalable analytical solutions. You will work closely with clients and business stakeholders to translate complex business challenges into data-driven strategies, while mentoring teams and ensuring successful delivery of end-to-end machine learning projects.

Responsibilities & Requirements

• Experience: 5 to 10 years
• Strong hands-on expertise in Python (mandatory)
• Experience in leading end-to-end ML engagements and managing client-facing projects
• Strong understanding of Machine Learning algorithms, model development, validation, deployment, and monitoring
• Team management and stakeholder handling experience
• Strong communication, presentation, and problem-solving skills
• Experience in Financial Services industry is a plus

Location: Noida

Why Join Us?

• Growth via the depth and breadth of data science projects for leading banks and fintech
• Opportunity to engage first-hand with clients and build strong industry relationships
• Collaborative work environment and mentorship from experienced colleagues
• Opportunity to drive real change through your work in a fast-growing niche consulting firm

Data Science Lead

Job Description Summary

The Data Science Lead is responsible for analysing, processing, and modelling data, then interpreting the results to create actionable plans that help solve business challenges and drive business results.

Job Description

This role will involve developing and deploying AI and machine learning models, working with large language models (LLMs), and putting analytical products into production.

This role reports to the Business Owner: Intelligent Analytics.

Key Result Areas:

Operational Delivery

• Gather data and enable a centralized data knowledge management system that integrates sources into one platform
• Develop a process to aggregate data and then enable centralization
• Mine and analyse large sets of data to derive actionable insights and value from data
• Develop an approach and model for foresight generation
• Perform predictive analytics and data modelling to derive insights about future business decisions that improve customer experiences and business revenue
• Design, implement, and support the delivery of data solutions
• Assess the effectiveness and accuracy of new data sources and data-gathering techniques
• Develop analysis metrics and new reports to support critical business decisions
• Provide input on optimal data architecture to support effective data solutions
• Develop relevant data models, including statistical and machine learning models, to derive more value from data
• Guide data types and content that should maximize value
• Collaborate with stakeholders to understand current challenges and needs that should be met with data solutions
• Identify data tasks and processes that can be improved and automated
• Develop real-time data gathering, analytics, and reporting capabilities
• Launch performance dashboards
• Enable automation
• Integrate with data roles to ensure a holistic GMPAS overview
• Develop and deploy AI and machine learning models
• Work with large language models (LLMs) and prompt engineering
• Put analytical products into production environments

Minimum Requirements

• Relevant degree or qualification.
• Extensive experience in data mining, modelling, and analytics.
• Deep knowledge of AI and machine learning techniques
• Experience with large language models (LLMs) and prompt engineering
• Sound knowledge of data analytics and reporting tools.
• Strong understanding of data models and architecture.
• Ability to integrate traditional research and insight with data.
• Proven track record of putting analytical products into production

Data Science Lead

Job Description Summary

The Data Science Lead is responsible for analysing, processing, and modelling data, then interpreting the results to create actionable plans that help solve business challenges and drive business results.

Job Description

This role will involve developing and deploying AI and machine learning models, working with large language models (LLMs), and putting analytical products into production.

This role reports to the Business Owner: Intelligent Analytics.

Key Result Areas:

Operational Delivery

• Gather data and enable a centralized data knowledge management system that integrates sources into one platform
• Develop a process to aggregate data and then enable centralization
• Mine and analyse large sets of data to derive actionable insights and value from data
• Develop an approach and model for foresight generation
• Perform predictive analytics and data modelling to derive insights about future business decisions that improve customer experiences and business revenue
• Design, implement, and support the delivery of data solutions
• Assess the effectiveness and accuracy of new data sources and data-gathering techniques
• Develop analysis metrics and new reports to support critical business decisions
• Provide input on optimal data architecture to support effective data solutions
• Develop relevant data models, including statistical and machine learning models, to derive more value from data
• Guide data types and content that should maximize value
• Collaborate with stakeholders to understand current challenges and needs that should be met with data solutions
• Identify data tasks and processes that can be improved and automated
• Develop real-time data gathering, analytics, and reporting capabilities
• Launch performance dashboards
• Enable automation
• Integrate with data roles to ensure a holistic GMPAS overview
• Develop and deploy AI and machine learning models
• Work with large language models (LLMs) and prompt engineering
• Put analytical products into production environments

Minimum Requirements

• Relevant degree or qualification.
• Extensive experience in data mining, modelling, and analytics.
• Deep knowledge of AI and machine learning techniques
• Experience with large language models (LLMs) and prompt engineering
• Sound knowledge of data analytics and reporting tools.
• Strong understanding of data models and architecture.
• Ability to integrate traditional research and insight with data.
• Proven track record of putting analytical products into production

Data Science Lead

Job Description Summary

The Data Science Lead is responsible for analysing, processing, and modelling data, then interpreting the results to create actionable plans that help solve business challenges and drive business results.

Job Description

This role will involve developing and deploying AI and machine learning models, working with large language models (LLMs), and putting analytical products into production.

This role reports to the Business Owner: Intelligent Analytics.

Key Result Areas:

Operational Delivery

• Gather data and enable a centralized data knowledge management system that integrates sources into one platform
• Develop a process to aggregate data and then enable centralization
• Mine and analyse large sets of data to derive actionable insights and value from data
• Develop an approach and model for foresight generation
• Perform predictive analytics and data modelling to derive insights about future business decisions that improve customer experiences and business revenue
• Design, implement, and support the delivery of data solutions
• Assess the effectiveness and accuracy of new data sources and data-gathering techniques
• Develop analysis metrics and new reports to support critical business decisions
• Provide input on optimal data architecture to support effective data solutions
• Develop relevant data models, including statistical and machine learning models, to derive more value from data
• Guide data types and content that should maximize value
• Collaborate with stakeholders to understand current challenges and needs that should be met with data solutions
• Identify data tasks and processes that can be improved and automated
• Develop real-time data gathering, analytics, and reporting capabilities
• Launch performance dashboards
• Enable automation
• Integrate with data roles to ensure a holistic GMPAS overview
• Develop and deploy AI and machine learning models
• Work with large language models (LLMs) and prompt engineering
• Put analytical products into production environments

Minimum Requirements

• Relevant degree or qualification.
• Extensive experience in data mining, modelling, and analytics.
• Deep knowledge of AI and machine learning techniques
• Experience with large language models (LLMs) and prompt engineering
• Sound knowledge of data analytics and reporting tools.
• Strong understanding of data models and architecture.
• Ability to integrate traditional research and insight with data.
• Proven track record of putting analytical products into production

Data Science Lead

Job Description Summary

The Data Science Lead is responsible for analysing, processing, and modelling data, then interpreting the results to create actionable plans that help solve business challenges and drive business results.

Job Description

This role will involve developing and deploying AI and machine learning models, working with large language models (LLMs), and putting analytical products into production.

This role reports to the Business Owner: Intelligent Analytics.

Key Result Areas:

Operational Delivery

• Gather data and enable a centralized data knowledge management system that integrates sources into one platform
• Develop a process to aggregate data and then enable centralization
• Mine and analyse large sets of data to derive actionable insights and value from data
• Develop an approach and model for foresight generation
• Perform predictive analytics and data modelling to derive insights about future business decisions that improve customer experiences and business revenue
• Design, implement, and support the delivery of data solutions
• Assess the effectiveness and accuracy of new data sources and data-gathering techniques
• Develop analysis metrics and new reports to support critical business decisions
• Provide input on optimal data architecture to support effective data solutions
• Develop relevant data models, including statistical and machine learning models, to derive more value from data
• Guide data types and content that should maximize value
• Collaborate with stakeholders to understand current challenges and needs that should be met with data solutions
• Identify data tasks and processes that can be improved and automated
• Develop real-time data gathering, analytics, and reporting capabilities
• Launch performance dashboards
• Enable automation
• Integrate with data roles to ensure a holistic GMPAS overview
• Develop and deploy AI and machine learning models
• Work with large language models (LLMs) and prompt engineering
• Put analytical products into production environments

Minimum Requirements

• Relevant degree or qualification.
• Extensive experience in data mining, modelling, and analytics.
• Deep knowledge of AI and machine learning techniques
• Experience with large language models (LLMs) and prompt engineering
• Sound knowledge of data analytics and reporting tools.
• Strong understanding of data models and architecture.
• Ability to integrate traditional research and insight with data.
• Proven track record of putting analytical products into production

Data Science Lead

Job Description Summary

The Data Science Lead is responsible for analysing, processing, and modelling data, then interpreting the results to create actionable plans that help solve business challenges and drive business results.

Job Description

This role will involve developing and deploying AI and machine learning models, working with large language models (LLMs), and putting analytical products into production.

This role reports to the Business Owner: Intelligent Analytics.

Key Result Areas:

Operational Delivery

• Gather data and enable a centralized data knowledge management system that integrates sources into one platform
• Develop a process to aggregate data and then enable centralization
• Mine and analyse large sets of data to derive actionable insights and value from data
• Develop an approach and model for foresight generation
• Perform predictive analytics and data modelling to derive insights about future business decisions that improve customer experiences and business revenue
• Design, implement, and support the delivery of data solutions
• Assess the effectiveness and accuracy of new data sources and data-gathering techniques
• Develop analysis metrics and new reports to support critical business decisions
• Provide input on optimal data architecture to support effective data solutions
• Develop relevant data models, including statistical and machine learning models, to derive more value from data
• Guide data types and content that should maximize value
• Collaborate with stakeholders to understand current challenges and needs that should be met with data solutions
• Identify data tasks and processes that can be improved and automated
• Develop real-time data gathering, analytics, and reporting capabilities
• Launch performance dashboards
• Enable automation
• Integrate with data roles to ensure a holistic GMPAS overview
• Develop and deploy AI and machine learning models
• Work with large language models (LLMs) and prompt engineering
• Put analytical products into production environments

Minimum Requirements

• Relevant degree or qualification.
• Extensive experience in data mining, modelling, and analytics.
• Deep knowledge of AI and machine learning techniques
• Experience with large language models (LLMs) and prompt engineering
• Sound knowledge of data analytics and reporting tools.
• Strong understanding of data models and architecture.
• Ability to integrate traditional research and insight with data.
• Proven track record of putting analytical products into production

Data Science Lead

Job Description Summary

The Data Science Lead is responsible for analysing, processing, and modelling data, then interpreting the results to create actionable plans that help solve business challenges and drive business results.

Job Description

This role will involve developing and deploying AI and machine learning models, working with large language models (LLMs), and putting analytical products into production.

This role reports to the Business Owner: Intelligent Analytics.

Key Result Areas:

Operational Delivery

• Gather data and enable a centralized data knowledge management system that integrates sources into one platform
• Develop a process to aggregate data and then enable centralization
• Mine and analyse large sets of data to derive actionable insights and value from data
• Develop an approach and model for foresight generation
• Perform predictive analytics and data modelling to derive insights about future business decisions that improve customer experiences and business revenue
• Design, implement, and support the delivery of data solutions
• Assess the effectiveness and accuracy of new data sources and data-gathering techniques
• Develop analysis metrics and new reports to support critical business decisions
• Provide input on optimal data architecture to support effective data solutions
• Develop relevant data models, including statistical and machine learning models, to derive more value from data
• Guide data types and content that should maximize value
• Collaborate with stakeholders to understand current challenges and needs that should be met with data solutions
• Identify data tasks and processes that can be improved and automated
• Develop real-time data gathering, analytics, and reporting capabilities
• Launch performance dashboards
• Enable automation
• Integrate with data roles to ensure a holistic GMPAS overview
• Develop and deploy AI and machine learning models
• Work with large language models (LLMs) and prompt engineering
• Put analytical products into production environments

Minimum Requirements

• Relevant degree or qualification.
• Extensive experience in data mining, modelling, and analytics.
• Deep knowledge of AI and machine learning techniques
• Experience with large language models (LLMs) and prompt engineering
• Sound knowledge of data analytics and reporting tools.
• Strong understanding of data models and architecture.
• Ability to integrate traditional research and insight with data.
• Proven track record of putting analytical products into production

Data Science Lead

Job Description Summary

The Data Science Lead is responsible for analysing, processing, and modelling data, then interpreting the results to create actionable plans that help solve business challenges and drive business results.

Job Description

This role will involve developing and deploying AI and machine learning models, working with large language models (LLMs), and putting analytical products into production.

This role reports to the Business Owner: Intelligent Analytics.

Key Result Areas:

Operational Delivery

• Gather data and enable a centralized data knowledge management system that integrates sources into one platform
• Develop a process to aggregate data and then enable centralization
• Mine and analyse large sets of data to derive actionable insights and value from data
• Develop an approach and model for foresight generation
• Perform predictive analytics and data modelling to derive insights about future business decisions that improve customer experiences and business revenue
• Design, implement, and support the delivery of data solutions
• Assess the effectiveness and accuracy of new data sources and data-gathering techniques
• Develop analysis metrics and new reports to support critical business decisions
• Provide input on optimal data architecture to support effective data solutions
• Develop relevant data models, including statistical and machine learning models, to derive more value from data
• Guide data types and content that should maximize value
• Collaborate with stakeholders to understand current challenges and needs that should be met with data solutions
• Identify data tasks and processes that can be improved and automated
• Develop real-time data gathering, analytics, and reporting capabilities
• Launch performance dashboards
• Enable automation
• Integrate with data roles to ensure a holistic GMPAS overview
• Develop and deploy AI and machine learning models
• Work with large language models (LLMs) and prompt engineering
• Put analytical products into production environments

Minimum Requirements

• Relevant degree or qualification.
• Extensive experience in data mining, modelling, and analytics.
• Deep knowledge of AI and machine learning techniques
• Experience with large language models (LLMs) and prompt engineering
• Sound knowledge of data analytics and reporting tools.
• Strong understanding of data models and architecture.
• Ability to integrate traditional research and insight with data.
• Proven track record of putting analytical products into production

Data Science Lead

Job Description Summary

The Data Science Lead is responsible for analysing, processing, and modelling data, then interpreting the results to create actionable plans that help solve business challenges and drive business results.

Job Description

This role will involve developing and deploying AI and machine learning models, working with large language models (LLMs), and putting analytical products into production.

This role reports to the Business Owner: Intelligent Analytics.

Key Result Areas:

Operational Delivery

• Gather data and enable a centralized data knowledge management system that integrates sources into one platform
• Develop a process to aggregate data and then enable centralization
• Mine and analyse large sets of data to derive actionable insights and value from data
• Develop an approach and model for foresight generation
• Perform predictive analytics and data modelling to derive insights about future business decisions that improve customer experiences and business revenue
• Design, implement, and support the delivery of data solutions
• Assess the effectiveness and accuracy of new data sources and data-gathering techniques
• Develop analysis metrics and new reports to support critical business decisions
• Provide input on optimal data architecture to support effective data solutions
• Develop relevant data models, including statistical and machine learning models, to derive more value from data
• Guide data types and content that should maximize value
• Collaborate with stakeholders to understand current challenges and needs that should be met with data solutions
• Identify data tasks and processes that can be improved and automated
• Develop real-time data gathering, analytics, and reporting capabilities
• Launch performance dashboards
• Enable automation
• Integrate with data roles to ensure a holistic GMPAS overview
• Develop and deploy AI and machine learning models
• Work with large language models (LLMs) and prompt engineering
• Put analytical products into production environments

Minimum Requirements

• Relevant degree or qualification.
• Extensive experience in data mining, modelling, and analytics.
• Deep knowledge of AI and machine learning techniques
• Experience with large language models (LLMs) and prompt engineering
• Sound knowledge of data analytics and reporting tools.
• Strong understanding of data models and architecture.
• Ability to integrate traditional research and insight with data.
• Proven track record of putting analytical products into production

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

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

Marsh is seeking a candidate for following position based out our office in Mumbai :

Senior Principal Engineer - Data Science

What can you expect?

This position is for an individual contributor in Data Science, who will develop and implement leading-edge techniques in artificial intelligence, machine learning, predictive modeling, and natural language processing as applied in commercial insurance and risk management. This position consults with senior colleagues on complex financial and statistical analyses, and develops approaches for new, market-leading tools.

Core Responsibilities:

• Develops modeling approaches for implementing new, market-leading analytics-based tools to understand and address risk
• Understands business problems to create an approach that starts with determining structured and unstructured data needs and availability, builds Machine Learning models, and finalizes with results that unlock insight for clients and colleagues
• Demonstrates skill in advanced statistical analysis, data mining, and/or research techniques, combined with broader awareness of the business and ongoing research, while functioning in a collaborative role with the Data Science team and across the wider organization
• Stays current with ongoing research in the field and brings new approaches to the team
• Serves as an internal expert resource and champion for data scien

Job Summary:

The Senior Manager, Data Science will lead a team of data scientists to develop advanced analytical models and drive data-driven decision-making across the organization. This role requires a strong blend of technical expertise, strategic thinking, and leadership skills. The Senior Manager will collaborate with cross-functional teams to identify business opportunities, design and implement innovative data solutions, and contribute to the company’s overall strategic goals.

Key Responsibilities:

Leadership and Team Management:

• Lead, mentor, and develop a team of data scientists.
• Foster a culture of innovation, collaboration, and continuous learning.
• Set clear goals and performance expectations for the team.

Strategy and Vision:

• Define and implement the data science strategy aligned with the company’s objectives.
• Identify and prioritize data science projects that drive business value.
• Stay abreast of industry trends and emerging technologies to keep the organization at the forefront of data science.

Technical Expertise:

• Oversee the development and deployment of machine learning models and algorithms.
• Ensure best practices in data modeling, coding, and experimental design.
• Provide technical guidance on complex data science projects.

Collaboration and Communication:

Tredence Inc.

5 days ago