Data Scientist - Forecasting & Pricing

Key Responsibilities\n• Demand Forecasting: Design, build, and deploy scalable demand forecasting models (time-series, ML-based) to predict product demand at SKU, category, channel, and regional levels.\n• Discount & Price Simulation: what-if simulation tools to optimize discount strategies and maximize margin.\n• End-to-End Model Ownership: Own the full ML lifecycle—data exploration, feature engineering, model training, validation, deployment, monitoring, and iteration.\n• Production Deployment on AWS: Build, train, and deploy models using AWS SageMaker; manage pipelines, endpoints, and model versioning in cloud-native environments.\n• Stakeholder Collaboration: Translate complex analytical outputs into clear, actionable insights for business leaders; present findings and recommendations to senior leadership.\n• Power BI: Create automated reports to present and track demand forecast model output.\n• Data Pipeline Development: Collaborate with Data Engineers to build robust, scalable data pipelines supporting model training and inference.\nMust-Have Skills\n• 6–8 years of hands-on experience in Data Science, ML, or Advanced Analytics\n• Strong experience in Demand Forecasting (ARIMA, Prophet, LSTM, XGBoost, or similar)\n• Proven expertise in Pricing/Discount Simulation (price elasticity modeling, scenario analysis)\n• Must have deep understanding of at least couple of Retail/CPG use cases such as customer segmentation, recommendations, demand forecasting, sentiment analysis, inventory optimization, promotion uplift modeling, campaign analysis, churn prediction, etc.\n• Hands-on production experience with AWS SageMaker (model training, hyperparameter tuning, deployment, batch/real-time inference)\n• Programming: Advanced Python (pandas, NumPy, scikit-learn, TensorFlow/PyTorch); SQL for data extraction and transformation\n• Statistical & ML Techniques: Regression, classification, time-series forecasting, ensemble methods, feature engineering