Director - Data Science and Machine Learning

Location: Mumbai

Experience: 9+ Years

Industry: Consumer Internet / Technology

Function: Data Science and Machine Learning

Job Description:

We are looking for an exceptional Data Science / Machine Learning leader to build and lead a high-performing Data Science organization and drive measurable business outcomes at scale.

This is a hands-on, player-coach leadership role for someone who can combine strategic thinking with strong technical execution. The individual will own the Data Science roadmap, identify high-impact business problems, lead experimentation, and build production-grade ML solutions that directly influence key business metrics.

The ideal candidate should be comfortable working across strategy, architecture, technical problem-solving, stakeholder management and team leadership.

Key Responsibilities:

- Define and drive the Data Science and ML roadmap by identifying and prioritizing high-impact business problems.

- Lead, hire and develop a high-caliber team of Data Scientists / ML Engineers.

- Own the complete ML lifecycle from problem definition and experimentation to deployment, monitoring and continuous improvement.

- Build scalable, production-grade ML systems with strong engineering practices.

- Drive ML solutions across areas such as demand forecasting, recommendation and personalization, pricing and optimization, customer analytics, marketing optimization, operational intelligence, capacity planning and decision automation.

- Partner closely with Product, Engineering and Business leadership to define success metrics and drive adoption of ML solutions.

- Establish best practices across feature engineering, experimentation, model validation, deployment, monitoring, retraining and governance.

- Act as a player-coach by providing technical direction while remaining hands-on when required.

- Build a high-velocity experimentation culture focused on measurable business impact.

- Improve overall ML maturity, adoption and technical standards across the organization.

Required Experience and Skills:

- 9+ years of experience in Data Science / Machine Learning.

- 2+ years of experience leading DS/ML teams or managing high-performing technical teams.

- Proven experience building and deploying ML systems in production with measurable business impact.

- Strong expertise in applied Machine Learning, statistical modelling and optimization.

- Strong hands-on proficiency in Python and SQL, along with Pandas, NumPy and relevant data visualization libraries.

- Experience with ML frameworks such as PyTorch, TensorFlow/Keras and Scikit-learn.

- Strong understanding of Regression, Classification, Tree-based / Gradient Boosting Models, Time-Series Forecasting, Ranking / Recommendation Systems, NLP and Optimization / Decision Systems.

- Strong understanding of MLOps and production ML, including Docker, REST APIs, CI/CD, version control, scalable inference and model lifecycle management.

- Experience with feature engineering pipelines, offline/online evaluation, A/B testing, model deployment and monitoring.

- Strong business orientation with the ability to translate ambiguous business problems into scalable ML solutions.

- Excellent communication and stakeholder management skills with the ability to influence senior leadership.

Good To Have:

- Experience in Time-Series Forecasting, Causal Inference, Market Mix Modelling or experimentation.

- Experience in recommendation / personalization, pricing, revenue optimization, logistics, supply chain or operational optimization.

- Experience in Consumer Internet, E-commerce, FoodTech, Mobility, Logistics or other high-scale operational businesses.

- Experience building ML platforms, reusable ML frameworks or internal DS productivity tools.

- Experience building decision automation and optimization systems.

Ideal Candidate:

We are looking for a strong player-coach who combines deep technical expertise with business acumen and leadership capability. The candidate should enjoy working on ambiguous, high-impact problems, building teams and taking ML solutions from concept to production at scale.