• Build, optimize, and deploy scalable forecasting models that support pricing, promotions, and assortment strategies across multiple product categories • Apply advanced statistical, machine learning, and deep learning methods (e.g., ARIMA, Prophet, gradient boosting, LSTMs, hybrid ensembles) for forecasting at SKU, category, and chain levels • Implement robust MLOps practices for model deployment, monitoring, and retraining using cloud platforms (Azure, GCP, AWS) • Integrate multiple internal and external data sources (e.g., coupon redemption, merchandising, competitive, and macroeconomic data) into forecasting pipelines • Collaborate with data engineering to ensure scalable, high-quality data pipelines • Partner with business stakeholders in pricing, promotions, and assortment to design and validate forecast-driven decision workflows • Coach and mentor junior data scientists, sharing best practices in forecasting, MLOps, and applied analytics • Monitor forecast accuracy, perform backtesting, and refine models to reduce error rates and improve stability • Develop frameworks for scenario planning and simulation to measure business impact of promotions, pricing strategies, and assortment changes