Data Scientist, Retail Demand Sensing

What you'll do

• Develop and maintain machine learning models to forecast product demand at SKU, store, category, and distribution center levels.
• Enhance demand sensing by integrating internal and external signals like promotions, weather, pricing, and market trends.
• Operationalize machine learning forecasting solutions to improve demand visibility and inventory decisions.
• Evaluate forecasting performance using accuracy metrics and continuously improve model effectiveness.
• Conduct root-cause analysis of forecast misses and recommend corrective actions.
• Build scalable analytics products to support replenishment decision-making and reduce working capital.
• Translate complex demand signals into actionable forecasts using advanced statistical techniques and machine learning.

What we're looking for

• 1+ years of experience in data science, machine learning, forecasting, or advanced analytics.
• Experience developing predictive or forecasting models in a business environment.
• Experience working with large-scale structured datasets.
• Proficiency in Python, SQL, and data manipulation frameworks.
• Knowledge of time-series forecasting techniques including ARIMA, Prophet, and gradient boosting.
• Experience with machine learning model development, validation, feature engineering, and statistical analysis.
• Proficiency with data visualization tools such as Power BI or Tableau.
• Bachelor's degree in Data Science, Statistics, Mathematics, Computer Science, Operations Research, Economics, Engineering, or a related field.
• Master's degree (preferred).
• Experience in retail, supply chain, inventory planning, replenishment, or merchandising analytics (preferred).
• Experience leveraging external demand drivers such as weather, events, seasonality, and pricing signals (preferred).