Sr. Product Manager - Tech, Amazon Pharmacy

Join Amazon Pharmacy's Supply Chain team and shape the systems that determine how medications reach patients. You will define product strategy and drive performance for ML-driven supply chain technology: demand forecasting models that predict prescription volume, procurement systems that optimize purchasing under expiry and regulatory constraints and placement algorithms that position inventory across fulfillment centers. Pharmacy supply chains are unlike retail: demand is driven by prescriptions (not browsing), products expire, controlled substances require compliance layers, and regulations vary by state. Every system you define and measure operates under these constraints. We are looking for a dynamic, analytically rigorous Senior Product Manager - Tech to own the product strategy for how Amazon Pharmacy forecasts demand, determines optimal inventory levels, decides what and when to buy, simulates network decisions, and identifies unhealthy inventory. You will define the problems, shape the solutions, and drive outcomes across these domains — working directly with engineers and scientists building the underlying ML and optimization systems. Key job responsibilities - Define the product vision and multi-year roadmap for pharmacy supply chain systems: demand forecasting, procurement, inventory placement and capacity planning - Translate complex supply chain and healthcare constraints (expiry, controlled substances, state regulations) into product requirements that engineering teams can build against - Design and own the end-to-end metrics framework for pharmacy supply chain health within your domain - Work with Operations, Compliance, Vendor Management, Fulfillment, Finance, and In-Stock partners to provide solutions and drive improvements - Partner with Applied Scientists and ML Engineers to define success criteria for forecasting models, optimization algorithms, and automated decision systems - Influence without authority across multiple teams and organizations to drive outcomes in a matrixed environment