Senior Data Scientist Supply Chain Forecasting & Demand Sensing

Role: Senior Data Scientist — Supply Chain Forecasting & Demand Sensing

Experience: 5+ years

Location: Bangalore, India (Hybrid)

We are sseeking a Senior Data Scientist to own demand forecasting and demand sensing end to end and turn forecasts into decisions the business acts on — working at the intersection of me-series forecasting, modern AI/ML engineering, and decision opmization. It is a hands-on role owning the full data science lifecycle: you scope the problem, engineer the features, build and validate the models, and ship them to production.

What You'll Own

• Time-series demand forecasting across products, locations, and horizons — capturing price, promotions, calendar/events, seasonality, weather effects etc.
• Demand sensing — short-horizon models fusing near-real-me signals (POS/sell-through, orders, shipments, inventory, weather, market signals) to catch near-term shifts and blend with the baseline forecast.
• Feature engineering & multi variate modelling — leakage-free feature pipelines and multi variate/causal models capturing driver interactions, and cannibalization/halo effects.
• The forecasting toolkit — statistical (ARIMA/ETS), ML (LightGBM/XGBoost), and deep-learning or probabilistic methods — choosing the right method for the problem, not the newest.
• The end-to-end DS lifecycle — framing, EDA, feature engineering, validation, deployment, monitoring, and explainability (SHAP), as a reproducible framework.
• Decisions & integration — translate forecasts into inventory, replenishment, fulfilment, and capacity actions; own the data contracts into the planning systems; and bring modern AI (LLMs/RAG/agents) to bear where it genuinely adds value.
  
  

Qualifications

• 5+ years applied DS delivering models used in production.
• Depth in me-series forecasting (ideally demand sensing) — evaluation, backtesting, failure modes.
• Feature engineering & multi variate/causal modelling with point-in-me correctness.
• Command of the DS lifecycle as a repeatable framework, not one-off notebooks.
• Supply chain / demand-planning domain (retail, manufacturing, or logis cs).
• Strong Python & SQL; solid software-engineering habits.
• Current AI/ML engineering — MLOps, monitoring, explainability, GenAI/LLM landscape.
• Businessmodeling translation and strong stakeholder communication.