Applied 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 (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

As an Applied Scientist on the P2OS team, your work directly shapes the prices customers see on hundreds of millions of Amazon products. In a given workweek, you might:

• 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