Research Scientist, Pricing Science

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 team builds the machine learning (ML) systems that power Amazon"s pricing decisions at scale: demand lift models, customer lifetime value (LTV) frameworks, and the experimentation infrastructure that validates whether pricing changes actually work.

We"re looking for a Research Scientist to own causal inference at the intersection of ML and pricing experimentation. Our team has identified a real gap: the methodological bridge between econometric analysis and production-scale ML pipelines needs a practitioner who lives in both worlds. You"ll build conditional average treatment effect (CATE) estimation models, design analysis workflows for pricing experiments, and develop reusable causal ML infrastructure that the broader team - including non-ML scientists - can rely on. If you"re a scientist who wants to tackle 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

• Design, train, evaluate, and deploy end-to-end causal ML pipelines for pricing use cases, including CATE estimation models and heterogeneous treatment effect frameworks.
• Serve as the team"s causal ML methodology expert - defining identification strategies, model selection criteria, evaluation standards, and the tradeoffs between econometric and ML approaches to causal estimation.
• Contribute causal analysis methodology to pricing A/B test post-analysis and build reusable tooling that economists and other non-ML scientists can adopt without requiring deep ML expertise.
• Assess emerging causal inference methods (e.g., synthetic difference-in-differences, generalized random forests, causal representation learning) for applicability to Amazon"s pricing context, and author internal methodology proposals for adoption.
• Partner with senior economists on identification strategy and causal assumptions, collaborate with software and data engineering partners on production deployment, and align with product managers on experiment design requirements.

A day in the life

Your work directly shapes the prices customers see on hundreds of millions of Amazon products. In a given week you might investigate an optimization anomaly in simulation and trace it to an unmodeled market dynamic, then design an offline evaluation framework to benchmark competing approaches before committing to online testing. You"ll collaborate with senior economists on identification strategy for a pricing experiment, present a science proposal for incorporating a new competitiveness signal into an optimization system, or work with the experimentation platform team on randomization design. You"ll also have opportunities to write up novel scientific findings for submission to venues such as KDD, NeurIPS, or the ACM Conference on Economics and Computation.

About the team

The P2 Optimization Science team sits at the center of Amazon"s pricing decisions. We build the ML systems, causal inference pipelines, and experimentation infrastructure that determine how prices are set across Amazon"s global catalog. Our team brings together research scientists, economists, and engineers who collaborate closely to turn rigorous methodology into production-quality systems with measurable business impact. We value scientific rigor, clear communication across disciplines, and a culture where every team member"s work is designed to be extensible and useful beyond their own project. If you want your science to directly influence how hundreds of millions of customers experience Amazon, this is the team to join.