P2Optimization Science (P2OS) is responsible for the ML models and analytical frameworks that drive pricing decisions at scale. The team spans demand lift modeling, pricing error detection, customer lifetime value, and experimentation. Our small team of specialized applied scientists and economists works closely alongside engineers, and pricing product managers.
Requirements
PhD in economics or equivalent
Experience in analytics and applied economics
Experience in developing and executing an analytic vision to solve business-relevant problems
Experience in building statistical models using R, Python, STATA, or a related software
Experience in industry, consulting, government or academic research
Experience with ML-adjacent causal methods (Double ML, causal forests, CATE estimation) and background in customer behavior modeling, LTV estimation, or long-run treatment effect estimation
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
Additional Information
Estimating the long-run customer value of a pricing decision is genuinely hard. The causal effects are delayed, noisy, and confounded by factors that standard experiment analysis wasn't designed to handle. Most pricing teams default to short-run metrics not because they don't care about long-run outcomes, but because measuring them rigorously is an unsolved problem.
P2OS is building the science to solve it. We're hiring a Sr. Economist to own that work - defining how we estimate customer lifetime value in a pricing context, building the identification strategies that make those estimates credible, and translating outputs into something pricing teams can use to make better decisions. The role sits at the intersection of econometric methodology and production-quality analysis, and requires someone who can operate independently in both.
As science lead, you'll own the LTV methodology domain, develop the economists and scientists on your scrum, and be the internal authority on causal inference for pricing across P2OS and partner teams.
Key job responsibilities
* Own the end-to-end LTV methodology for pricing - identification strategy, modeling choices, validation approach, and business use cases - and drive adoption across pricing contexts
* Deliver high-stakes analyses connecting LTV estimates to a concrete pricing decision and strategy change at VP+ level
* Apply advanced causal methods to live pricing problems; document approaches so the team can build on and extend them.
* Provide causal inference guidance on pricing experiment questions as they arise - being the methodology resource when experiments generate LTV-relevant questions
* Serve as cross-team economic advisor to Finance, Customer Behavior, and Demand Science on LTV assumptions and causal identification
* Actively mentor junior scientists, earn trust of cross-functional tech and product partners.
A day in the life
In a typical day, you'll move between methodology work and stakeholder-facing analysis.
- On the science side, that means reviewing identification assumptions with the Causal AS, validating estimation choices for the LTV framework, and documenting methodology decisions in ways that non-economists can act on.
- On the applied side, you'll be in rooms with Finance, Pricing PMs, and other science teams: aligning on LTV definitions, resolving disagreements between competing metrics, and translating causal findings into recommendations that land in strategy reviews.
- As tech lead, you need to work to develop the economists and scientists on your scrum: structured reviews, identification strategy feedback, and raising the quality of analyses before they reach stakeholders.
The mix shifts, but the through-line is to progress the LTV methodology from open questions to shipped frameworks, and making sure the team's causal work is rigorous enough to hold up when it counts.
Amazon.com · Seattle, WA