role:
data science leader
experience:
10-12 years of overall experience, with at least 5+ years in data science roles along with 2-4 years in a leadership or managerial capacity.
technical skills:
data science & machine learning
- deep understanding of statistical modeling, predictive analytics, clustering, nlp, and time series forecasting.
- strong grasp of model evaluation, fairness, explainability, and business alignment.
programming
- proficient in python (or r) with experience in data science libraries like pandas, numpy, scikit-learn, tensorflow, or pytorch.
- ability to read and review code, debug, and advise on best practices.
cloud computing
- working knowledge of at least one major cloud platform (aws, gcp, azure).
- experience with cloud-native tools for data storage (s3, bigquery), compute (ec2, gke, lambda), and ml services (sagemaker, vertex ai, azure ml).
data engineering fundamentals
- understanding of data pipelines, etl/elt processes.
- familiarity with tools like airflow, dbt, spark, sql.
data visualization
- experience with dashboards and reporting tools (tableau, power bi, looker, or custom visualizations using plotly/altair).
- skilled in transforming complex outputs into clear, compelling narratives for non-technical stakeholders.
mlops / deployment
- familiarity with modern ml development and deployment practices.
- basic understanding of deploying models to production, ci/cd pipelines, and monitoring.
generative ai
- familiarity with genai concepts, including large language models (llms), embeddings, prompt engineering, and rag pipelines.
- familiarity with tools and apis like openai, hugging face, langchain.
leadership & people management
team management
- experience leading and mentoring teams of 5-10 individuals across varying levels.
- proven track record of building and scaling data science teams, delivering impactful projects
- conduct performance reviews, manage career growth, and foster a healthy team culture.
cross-functional collaboration
- proven ability to work closely with product, engineering, marketing, and business teams.
hiring & talent development
- skilled in identifying top talent, conducting interviews, onboarding, and team capability building.
project & stakeholder management
project management
- experienced in managing multiple projects simultaneously.
- comfortable with agile methodologies, sprint planning, and delivery tracking.
stakeholder communication
- translating technical insights into business terms.
- communicates technical insights clearly to senior executives.
problem solving & scope management
- ability to break down ambiguous business problems into solvable components.
- define scope and ensure projects align with business impact.
strategic thinking
- aligns team objectives with company vision and business goals.
- drives roadmap planning, and long-term capability building.
business acumen
domain knowledge
- deep understanding of any one business vertical among the following e.g., telecom, bfsi, fintech, e-commerce, healthcare etc.
impact orientation
- focus on delivering measurable business value from data science efforts.
- strong grasp of metrics, kpis, and roi-driven thinking.
strategic thinking
- ability to align data science efforts with long-term business goals.
soft skills
- exceptional communication, both verbal and written.
- decision-making: balanced between data, intuition, team input and strategic vision.
- empathy & emotional intelligence: especially for managing team dynamics and motivation.
- adaptability: in the face of changing priorities or business goals and organizational change.
