Director - Data Science

As a Data Scientist, you will take end-to-end ownership of highly complex Machine Learning and Deep Learning initiatives focused on supply chain optimisation, demand forecasting, and operational efficiency. You will bridge advanced statistical research and production-grade ML implementation to solve critical business challenges across inventory management, demand planning, logistics, and resource allocation. Operating with minimal supervision, you will drive the development of scalable predictive models and optimisation algorithms, mentor Data Scientists, and partner with Product and Engineering leaders to translate complex business problems into impactful data-driven solutions.

Responsibilities:

• End-to-End ML Execution: Lead the design, development, deployment, and optimisation of advanced ML and Deep Learning models for supply chain challenges, including demand forecasting, inventory optimisation, capacity planning, and supply-demand balancing.
• Demand Forecasting: Develop scalable forecasting models to predict demand across SKU, category, geography, channel, and regional levels, leveraging advanced time-series techniques and Deep Learning approaches.
• Operations Optimisation: Design and implement optimisation algorithms for inventory allocation, transportation planning, resource utilisation, replenishment, and logistics efficiency.
• Advanced Modelling: Apply statistical modelling, probabilistic forecasting, causal inference, and ML techniques to improve forecast accuracy, minimise operational costs, and drive business outcomes.
• Mentorship: Guide and mentor Data Scientists, ensuring high standards of statistical rigour, model quality, experimentation, and scalable ML architecture.
• Cross-Functional Strategy: Collaborate with Product Managers, Supply Chain teams, and Engineering Leaders to identify business opportunities and shape data-driven product roadmaps.
• Production ML: Drive the productionization of large-scale forecasting and optimisation solutions, ensuring model scalability, reliability, and measurable business impact.
• Applied Research: Explore and implement state-of-the-art ML and Deep Learning techniques for forecasting, optimisation, and supply chain analytics.

Requirements:

• M. Tech, M. S., or PhD in Computer Science, Machine Learning, Statistics, Mathematics, Operations Research, or a closely related field.
• 7-15 years of industry experience in Data Science and Machine Learning, with a proven track record of deploying large-scale ML solutions into production.
• Strong hands-on experience in Supply Chain Analytics, Demand Forecasting, Time-Series Modelling, or Operations Research / Optimisation.
• Deep expertise in ML and Deep Learning frameworks such as TensorFlow and PyTorch.
• Strong understanding of forecasting algorithms, statistical modelling, optimisation techniques, and complex ML algorithms.
• Exceptional programming skills in Python and experience with ML libraries such as Scikit-learn, XGBoost, LightGBM, and relevant Deep Learning frameworks.
• Experience building scalable ML pipelines and production-grade predictive models.
• Strong problem-solving skills with the ability to translate complex supply chain business problems into ML-driven solutions.

Skills

Data Science, Deep Learning, Forecasting, Machine Learning, Operations Research, Optimization, PyTorch, Python, Scikit-learn, Statistics, Supply Chain, TensorFlow, Time-Series, XGBoost