Applied Scientist, Pricing Science

Amazon • Seattle, WA

4d • Onsite

About The Position

The P2 Optimization Science (P2OS) team builds the machine learning systems that power Amazon's pricing decisions at scale, focusing on demand lift models, customer lifetime value frameworks, and experimentation infrastructure. This role specifically focuses on causal inference at the intersection of ML and pricing experimentation, bridging the gap between econometric analysis and production-scale ML pipelines. The position emphasizes shipping production-quality causal pipelines with real business impact, rather than pure research. Success will be measured by improvements in LTV estimates, avoidance of pricing errors, and the usability of built tools for economists.

Requirements

• 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

Nice To Haves

• 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.

Responsibilities

• Build causal ML pipelines for pricing, including designing, training, evaluating, and deploying end-to-end causal estimation models.
• Own the science of heterogeneous treatment effects, serving as the team's subject matter expert on causal ML methodology, identification strategies, model selection, evaluation standards, and the trade-offs between econometric and ML approaches.
• Support pricing experiment analysis by contributing causal analysis methodology to pricing weblab and A/B test post-analysis, and by building reusable tooling for economists.
• Connect model outputs to business outcomes by defining business metrics each model moves before coding and delivering evaluation reports focused on pricing errors avoided and LTV estimate changes.
• Evaluate and adopt novel techniques, assessing the applicability of emerging causal inference methods to Amazon's pricing context and writing internal methodology proposals.
• Write internal documentation and methodology papers, producing at least one internal write-up per half that connects a causal ML technique to a concrete pricing use case, and ensuring pipelines are extensible and well-documented.
• Collaborate with economists on identification strategy and causal assumptions, work with engineers on production deployment, and align with product managers on experiment design requirements.

Benefits

• 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
• parental leave
• sign-on payments
• restricted stock units (RSUs)

What This Job Offers

• Job Type: Full-time
• Career Level: Senior