Pricing Optimization Scientist, Pricing Science

Take hard sequential decision problems, like delayed rewards, shifting demand and strict business constraints, and turn them into pricing policies that run at Amazon scale. Few places let you test RL ideas on a problem this large.

Key job responsibilities

Every day, Amazon makes hundreds of millions of pricing decisions. Each one is a choice made with incomplete information: demand shifts, competitors move, inventory runs low, and every price we set changes the data we learn from next. The P2 Optimization Science (P2OS) team builds the science behind these decisions. Our work includes demand response models, the optimization systems that turn them into prices, and the experiments that prove those prices help customers and the business.

We're hiring an Applied Scientist to build sequential decision-making methods for pricing. Most pricing systems estimate demand first and optimize second. We want systems that learn while they decide. That means deciding when to explore and when to exploit, how to trade short-term revenue against long-term customer trust, and how to set safe prices in categories where we have little history. You'll work on contextual bandits, reinforcement learning and online optimization problems that most research setups ignore: delayed and noisy rewards, hard business constraints, non-stationary demand, and a catalog too big for a separate model per product.