At a glance
Experience
10-15 years
Seniority
Senior Leadership
Discipline
Select Specialization
Market
India
01
Role Overview
A concise summary of the role, its purpose and the contribution expected from the person in the position.
The Head of Data Science leads the organization’s data science and AI strategy, translating business problems into scalable machine learning and advanced analytics solutions. The role owns the data science roadmap, model lifecycle, team capability, and responsible AI governance, partnering with product, engineering, analytics, compliance, and business leaders. It drives measurable impact through production-grade models, data-driven decision-making, and a culture of experimentation while ensuring ethical, secure, and compliant use of data and AI across the enterprise.
02
Key Responsibilities
Use these as a starting point and prioritize the outcomes, activities and responsibilities that matter for the position.
- Define and own the enterprise data science and AI strategy, roadmap, and portfolio in alignment with business priorities and measurable outcomes.
- Translate business problems into data science use cases, success metrics, and delivery plans with product, engineering, operations, and commercial stakeholders.
- Lead, hire, mentor, and scale a multidisciplinary team of data scientists, ML engineers, analysts, and applied AI specialists.
- Establish model development standards, experimentation practices, code quality, reproducibility, and documentation across the model lifecycle.
- Oversee data acquisition, feature engineering, model training, validation, deployment, monitoring, and retraining with engineering and MLOps teams.
- Prioritize projects using business impact, feasibility, risk, data readiness, and time-to-value; manage trade-offs and executive expectations.
- Drive productionization of machine learning and AI solutions, including APIs, batch pipelines, real-time scoring, and integration with business systems.
- Partner with product teams on AI product management, requirements, user adoption, measurement, and iterative improvement.
- Ensure responsible AI and compliance through bias assessment, explainability, privacy, security, model risk controls, and audit readiness.
- Collaborate with data governance and IT teams on data quality, lineage, access controls, metadata, and master data management.
- Communicate insights and recommendations to executive leadership, boards, clients, and regulators through clear narratives and dashboards.
- Manage budgets, vendor relationships, cloud/ML platform investments, and external research or consulting partnerships.
- Build a culture of experimentation, continuous learning, knowledge sharing, and ethical AI practice across the organization.
- Track industry trends in LLMs, generative AI, causal inference, and automation; assess build-vs-buy options and incubate high-potential capabilities.
- Define KPIs for team productivity, model performance, business lift, adoption, and risk; report progress and outcomes to stakeholders.
03
Required Experience and Qualifications
Define the experience, qualifications and capabilities needed for success in this position. Separate essential criteria from preferences.
Core requirements
- 10–15 years of experience in data science, machine learning, advanced analytics, or AI, including 5+ years in people and portfolio leadership.
- Strong foundation in statistics, probability, experimentation, causal inference, and machine learning algorithms.
- Proficiency in Python or R, SQL, and modern data science tooling; working knowledge of software engineering practices.
- Experience deploying and monitoring ML models in production using cloud platforms (AWS, Azure, or GCP) and MLOps tooling.
- Demonstrated track record of translating AI/ML use cases into measurable business impact and managing a portfolio of projects.
- Experience building, hiring, and developing high-performing data science teams in matrixed organizations.
- Strong stakeholder management with product, engineering, business, compliance, and executive audiences.
- Knowledge of data management, data governance, privacy, model risk, and responsible AI frameworks.
- Ability to communicate complex technical concepts to non-technical leaders and influence strategic decisions.
- Experience with AI product management, project delivery, vendor evaluation, and budget ownership.
- Exposure to generative AI/LLM applications, evaluation, safety, and operational controls is preferred.
- Bachelor’s or Master’s in a quantitative field; MBA, PhD, or equivalent leadership training is advantageous.
04
Education and Credentials
Include education, licences, certifications or other credentials when relevant to the work. Distinguish essential requirements from preferred qualifications.
Education
- Degree in Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related quantitative field or a related field.
- Degree in MBA or equivalent management qualification preferred or a related field.
- Degree in PhD in a relevant quantitative or AI discipline preferred or a related field.
Certifications and credentials
- Certified Data Scientist (CDS) certification.
- AWS Certified Machine Learning – Specialty certification.
- Google Professional Machine Learning Engineer certification.
- Microsoft Certified: Azure Data Scientist Associate certification.
- Databricks Certified Machine Learning Professional certification.
- Certified AI Governance Professional (CAIGP) certification.
- ISO/IEC 42001 AI Management System Lead Auditor certification.
- Project Management Professional (PMP) certification.
Hiring guidance: Keep criteria relevant to the position and avoid using a degree or certification as a hard filter when equivalent practical capability is acceptable.
05
Skills
Organize relevant capabilities into technical, functional and soft skills where these categories apply to the position.
Each category separates the capabilities needed for the role from additional skills that add value. These examples can be edited for the specific position and industry.
01
Technical Skills
Tools, technologies, methods or specialist knowledge relevant to the work.
MandatoryEssential
Python
R
SQL
Machine Learning
deep learning
Statistics and Probability
Experiment Design and A/B Testing
MLOps
Cloud platforms (AWS/Azure/GCP)
Data Pipelines and Feature Engineering
Model Monitoring and Retraining
Generative AI and LLM Fundamentals
Responsible AI and Model Risk Controls
AI Product Management
Cross-functional Collaboration
Change management
PreferredAdds value
PyTorch
TensorFlow
Scikit-learn
Spark
Databricks
Snowflake
Kubernetes
Docker
LLMOps
Vector Databases
AutoML
Causal Inference
Time Series Forecasting
NLP
Computer Vision
MLflow
Airflow
Kubeflow
Agile Delivery
Financial Modeling
Public Speaking
02
Functional Skills
Capabilities used to carry out responsibilities and deliver expected outcomes.
MandatoryEssential
Data Science Strategy and Roadmap
Data Management and Governance
AI Compliance and Responsible AI
AI Project and Portfolio Management
Client and stakeholder management
Business KPI Definition and ROI Analysis
Team and Talent Management
Vendor and Budget Management
PreferredAdds value
Industry-specific Analytics
Consulting and Advisory
Product Lifecycle Management
AI Governance Frameworks (NIST AI RMF, ISO/IEC 42001)
Privacy Regulations (DPDP Act, GDPR)
Change Management Programs
03
Soft Skills
Behaviours that support collaboration, communication, ownership and effective execution.
MandatoryEssential
Executive communication
Strategic Thinking
People Leadership and Mentoring
Problem Framing
Ethical Judgement
Decision Making Under Ambiguity
PreferredAdds value
Thought Leadership
negotiation
Cross-Cultural Team Leadership
Coaching and succession planning
Innovation mindset
Keep criteria realistic: Mark a skill as Mandatory only when it is genuinely essential for the role. Preferred skills should strengthen a match, not automatically disqualify a candidate.
06
Ready-to-Use Boolean Search Query
Copy this starting query into a job portal or sourcing tool, then edit it to match your exact hiring criteria.
Pair the query with portal filters
Experience: 10-15 yearsLocation: target citiesNotice period: if requiredEmployment type: role-specific
07
Top 5 Interview Questions
Use these questions to assess practical judgement, role knowledge and applied problem-solving. Expand each one for answer signals.
1. How do you align a data science portfolio with business strategy?
Look for: Strong answers cover business discovery, executive sponsorship, KPI definition, impact-feasibility prioritization, roadmap governance, and regular value tracking. They should mention balancing quick wins with strategic bets and stopping low-value projects.
2. Walk us through how you take a model from concept to production.
Look for: Candidates should describe problem framing, data quality, baseline/model selection, validation, deployment with engineering/MLOps, monitoring, retraining, documentation, and adoption measurement. Include risk and rollback plans.
3. How do you build and scale a high-performing data science team?
Look for: Look for hiring mix, career paths, mentoring, technical standards, rituals, retention, and collaboration with product/engineering. Strong answers address capacity planning and creating psychological safety.
4. How do you operationalize responsible AI and compliance?
Look for: Expected points include AI governance councils, risk classification, bias/fairness testing, explainability, privacy/security, model documentation, audit trails, monitoring, and alignment with NIST AI RMF or ISO/IEC 42001.
5. How do you prioritize data science projects when resources are constrained?
Look for: Strong responses use impact, feasibility, data readiness, risk, strategic fit, and time-to-value. They mention transparent scoring, stakeholder alignment, quick wins, dependencies, and rebalancing as conditions change.
Interviewing guidance: Adapt follow-up questions to the candidate's experience and the actual system. Use the same core criteria consistently across candidates.
08
Career Path and Progression
Show how this role may progress over time, including adjacent paths and possible next-step roles.
Demand for Head of Data Science roles in India remains strong as enterprises, GCCs, startups, and regulated industries scale AI and analytics. Organizations need leaders who can move beyond pilots to production-grade ML, manage AI risk, and demonstrate ROI. Growth is strongest in IT services, BFSI, healthcare, retail, manufacturing, and telecom, with rising demand for generative AI, MLOps, and responsible AI expertise. The role is increasingly strategic, with pathways to VP/Chief Data Officer, Chief AI Officer, or broader digital transformation leadership.
Data Analyst
Data Scientist
Senior Data Scientist
Lead Data Scientist / Principal Data Scientist
Data Science Manager
Director / Head of Data Science
VP of Data Science / Chief Data Officer / Chief AI Officer
09
History and Background
Use this section for relevant history, origins, evolution or background related to the role, discipline, technology, business function, industry or process.
The role emerged as organizations moved from descriptive analytics and isolated statistical modeling to enterprise-scale machine learning, AI products, and data-driven operations. Early data science leaders often sat within analytics or R&D teams, focusing on model accuracy and exploratory insight. As cloud platforms, big data, MLOps, and generative AI matured, companies needed a senior owner to connect data science with product strategy, engineering delivery, risk, and commercial outcomes. The Head of Data Science has since evolved into a cross-functional leadership position responsible for talent, platform investments, AI governance, and measurable business value—not only technical modeling—across industries adopting AI at scale.
10
Salary and Compensation Context
India market benchmark for this role. Compensation varies by experience, location, organization, industry and scope.
Mean total pay
₹52.5 LPA
Median total pay
₹48.0 LPA
Observed maximum
₹95.0 LPA
How to read this benchmark The mean and median are indicative total-pay figures for Head of Data Science roles in India. The maximum is an observed reported value, not a guaranteed market ceiling. Actual compensation should be calibrated to the role's experience range, location, company, industry, skills and responsibilities.
Market snapshot: November 2026. Indicative compensation data compiled from verified industry sources. Benchmark methodology and sample coverage can change over time.
Common role questions
Head of Data Science FAQs
What is the typical experience required for a Head of Data Science in India?
Most employers seek 10–15 years in data science, machine learning, or advanced analytics, including at least 5 years leading teams and owning portfolios. Experience with production ML, stakeholder management, and AI governance is usually expected.
What does a Head of Data Science do day to day?
They set strategy, prioritize use cases, review model development and deployment, manage team capacity, engage business stakeholders, oversee governance and compliance, and report impact to leadership. The role balances technical direction with people and portfolio management.
How is Head of Data Science different from Data Science Manager?
A Data Science Manager typically leads a team and delivers projects within a defined area. The Head of Data Science owns the enterprise-wide strategy, portfolio, operating model, governance, budgets, and executive relationships, often across multiple teams.
Which industries hire Heads of Data Science in India?
Hiring spans IT and GCCs, BFSI, healthcare, retail and e-commerce, manufacturing, telecom, energy, logistics, and professional services. Any organization scaling AI, analytics, or data products may create this role.
What is the salary range for a Head of Data Science in India?
Compensation varies by company size, industry, and location. Indicative annual pay is around INR 48–95 LPA, with a median near INR 48 LPA and mean near INR 52.5 LPA for senior leadership roles; top packages can exceed this in large tech or global capability centers.
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