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.

Were 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. Youll 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.

This is an applied role. Youll take methods from offline simulation through online experiments and into production. Your success will be measured by what changes for customers and the business not by benchmark scores. Youll partner closely with senior scientists economists software engineers and product managers. Youll also help shape the teams long-term direction on how Amazon prices adaptively.

If you like hard optimization problems with real stakes and youd rather ship a method that works than publish one that doesnt wed like to talk to you.

A day in the life
As a Pricing Applied Scientist youll shape the prices customers see on a very large share of Amazons a typical week you might:

- Prototype a contextual bandit policy for a product segment with sparse sales history then stress-test it in simulation against the current production approach.
- Dig into why a policy is exploring more than expected and trace it to a reward signal that arrives days late.
- Work through a reward design with an economist: how to credit a price change that lifts conversion today but may affect repeat purchases next month.
- Review an online experiment readout with your product manager and decide whether the policy is ready to scale or needs another iteration.
- Write a short science proposal on a new constraint like limiting how often a price can change and estimate its cost to the objective.
- Pair with an engineer to make a policy fast enough to update prices in near real time.
- Present your findings at the teams science review or develop them into a paper for a venue like KDD NeurIPS or EC.

About the team
The Pricing and Promotions Optimization Science (P2OS) team is the Optimization Application Layer within Pricing Sciences. Our team is a small unit of specialized science resources equipped to solve some of the organizations most ambiguous challenges. Our charter is to develop models to set optimal prices and promotions shared common tooling across all merchants rule and policy setting to drive error detection and price sharpness and innovative price guardrails and incentives to support long-term marketplace health.

- PhD or Masters degree and 4 years of CS CE ML or related field experience
- Experience programming in Java C Python or related language
- Experience in any of the following areas: algorithms and data structures parsing numerical optimization data mining parallel and distributed computing high-performance computing

- Experience in professional software development
- Experience using Unix/Linux
- Knowledge of architectural concepts and algorithms schedule tradeoffs and new opportunities with technical team members

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status disability or other legally protected status.

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The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience qualifications and location. Amazon also offers comprehensive benefits including health insurance (medical dental vision prescription Basic Life & AD&D insurance and option for Supplemental life plans EAP Mental Health Support Medical Advice Line Flexible Spending Accounts Adoption and Surrogacy Reimbursement coverage) 401(k) matching paid time off and parental leave. Learn more about our benefits at WA Seattle - 142800.00 - 193200.00 USD annually