Applied Scientist, Pricing Science

Description Pricing is one of the most consequential decisions Amazon makes — and the science behind it needs to be causally rigorous, not just predictive. The P2 Optimization Science (P2OS) team builds the machine learning systems that power Amazon's pricing decisions at scale: demand lift models, customer lifetime value frameworks, and the experimentation infrastructure that validates whether our pricing changes actually work. We’re hiring an Applied Scientist to own causal inference at the intersection of ML and pricing experimentation. This role exists because our team has identified a real gap: the methodological bridge between econometric analysis (owned by our economists) and production‑scale ML pipelines (owned by our engineers) needs a practitioner who lives in both worlds. You’ll build CATE estimation models, design analysis workflows for pricing weblabs, and develop the reusable causal ML infrastructure that the broader team — including non‑ML scientists — can rely on. This is not a research role. The bias here is toward shipping production‑quality causal pipelines with real downstream business impact. You’ll measure success by what changes in LTV estimates, what pricing errors your models help avoid, and whether the economists on your team can actually use what you build. If you’re a scientist who wants to work on hard causal identification problems in a high‑stakes production environment — and who finds satisfaction in making rigorous methods accessible to a broader team — this role is for you. Key job responsibilities Build causal ML pipelines for pricing — Design, train, evaluate, and deploy end‑to‑end causal estimation models for pricing use cases. Own the science on heterogeneous treatment effects — Be the team SME on causal ML methodology: identification strategies, model selection, evaluation standards, and the tradeoffs between econometric and ML approaches to causal estimation. Support pricing experiment analysis — Contribute causal analysis methodology to pricing weblab and A/B test post‑analysis; build reusable tooling that economists can use without requiring ML expertise. Connect model outputs to business outcomes — Define, before writing code, what business metric each model moves; deliver model evaluation reports framed around pricing errors avoided and LTV estimate changes. Evaluate and adopt novel techniques — Assess applicability of emerging causal inference methods (synthetic DiD, generalized random forests, causal representation learning) to Amazon's pricing context; write internal methodology proposals for adoption. Write internal documentation and methodology papers — Produce at least one internal write‑up per half that connects a causal ML technique to a concrete pricing use case; make pipelines extensible and well‑documented so other scientists can build on them. Collaborate across disciplines — Partner closely with the Sr. Economist on identification strategy and causal assumptions; work with SDE and DE partners on production deployment; align with PMs on experiment design requirements. A day in the life Investigate an optimization anomaly in simulation and trace it back to a model input gap or an unmodeled market dynamic. Design an offline evaluation framework to benchmark competing optimization approaches before committing to online testing. Collaborate with Sr. Economists on the identification strategy for the model you’re building for a pricing lab. Present a science proposal for incorporating a new competitiveness or inventory signal into an optimization system. Work cross‑team with the experimentation platform team on randomization design. Develop and write up a novel scientific finding — preparing a paper or technical report for submission to a top‑tier venue such as KDD, NeurIPS, or the ACM Conference on Economics and Computation. Basic Qualifications PhD, or Master’s 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. Preferred Qualifications Experience using Unix/Linux. Experience in professional software development. Usage of generative AI tools to enhance workflow efficiency, with a willingness to learn effective prompting and evaluation practices. Ability to recognize opportunities where generative AI could enhance products, workflows, or customer experiences. Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner. 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 USA, WA, Seattle - 142,800.00 - 193,200.00 USD annually #J-18808-Ljbffr Amazon Science