Sr. Economist, Pricing Science

Amazon; Culver City (CA); On-site; USD 159,200 - 215,300; Full time

Benefits offered by this job

Health insurance

401(k) matching

Paid time off

Parental leave

Job summary

Amazon is hiring a Sr. Economist in Culver City, California, to take charge of the customer lifetime value (LTV) methodology for pricing. This role involves utilizing econometric methods to drive pricing decisions, mentor junior team members, and liaise with various departments. Ideal candidates will have a PhD in economics, experience in applied analytics, and proficiency in statistical software such as R and Python. Comprehensive benefits and a competitive salary of $159,200 to $215,300 annually are offered.

Qualifications

• Strong analytical skills and experience in causal inference methods.
• Experience with statistical software like R, Python, or STATA.
• PhD in economics with a focus on applied analytics.

Responsibilities

• Own the LTV methodology for pricing and drive adoption.
• Deliver analyses connecting LTV estimates to pricing decisions.
• Mentor junior scientists and lead cross-team collaborations.

Skills

Analytical skills

Causal inference

Econometric methodology

Stakeholder analysis

Education

PhD in economics or equivalent

Tools

R

Python

STATA

Job description

Job ID: 10414297 | Amazon.com Services LLC

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 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.

About the team

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.

Basic qualifications

• PhD in economics or equivalent

Preferred qualifications

• 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

Benefits

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 https://amazon.jobs/en/benefits .

Compensation

USA, WA, Seattle - 159,200.00 - 215,300.00 USD annually

Posted: April 30, 2026 (Updated 8 days ago)

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.