Decision Science

This role makes forecasting an operational decision problem rather than an isolated statistical exercise. Demand and labor forecasts influence staffing commitments, capacity allocation, cost and customer service across Amazon's grocery network. The relevant objective is not simply to improve model accuracy, but to determine whether better forecasts produce better labor and capacity decisions at an acceptable operating cost. Value depends on evaluating forecast errors according to their downstream consequences, including the different costs of understaffing and overstaffing, and identifying which teams bear those consequences. The main failure mode is optimizing aggregate forecasting metrics while making staffing or capacity decisions worse for specific locations, periods or business stakeholders. The posting demonstrates investment in applied forecasting science, not independently verified operational improvement.