Senior Economist

Description

If you are an economist who dreams of shaping an Amazon-wide measurement program and possess the confidence to navigate early-stage ambiguity, read on.

Amazon's customers rely on the completeness, consistency, and correctness of Amazon's product data to make well-informed purchase decisions. Improving the quality of that product data is a continuous effort spanning hundreds of initiatives across the company — and understanding how much those improvements actually move the needle for customers and the business is one of the hardest and most valuable problems in the space.

Amazon Selection Catalog Systems (ASCS) is looking for a Senior Economist to join the Catalog Experimentation and Impact Measurement team. Our mission is to be the source of truth for the causal impact of catalog quality improvements across Amazon. We answer a deceptively simple question at massive scale: when we make product data better, how much do customers benefit and how much incremental business does it drive?

We do this through two complementary approaches. First, we design and run controlled experiments that establish rigorous, causal links between improvements in product data quality and customer and business outcomes — turning what used to be slow, lagging estimates into fast, decomposable, decision-ready measurement. Second, we operate a federated measurement model: partner teams across Amazon run their own experiments and analyses for their launches, and we set the scientific standard — advising on experimental design, reviewing methodology, and aggregating results into a single, trusted view of total impact for leadership.

We are looking for an economist with demonstrated experience conducting causal inference at scale. Our problems center on attributing value to actions in the complex world of catalog information and customer behavior — isolating causal effects amid noise, selection, and interference, and doing so credibly enough to guide decisions at the highest levels. The ideal candidate combines deep acumen in econometrics, experimental design, and data science to grapple with these challenges and to raise the measurement bar for the entire organization.

This is an opportunity to influence catalog quality improvements across Amazon and to build a measurement program from the ground up — one with the rigor, transparency, and scale to become the standard the whole company trusts. You will not only measure impact; you will define how Amazon measures it.

Key job responsibilities
Design and run experiments at scale. Conceive, design, and execute controlled experiments that establish rigorous causal links between improvements in product data quality and customer and business outcomes, producing fast, decomposable, decision-ready measurement.

Develop causal models and methodology. Build and refine econometric and causal-inference methods to attribute incremental impact to catalog actions amid noise, selection, and interference — and advance the state of the art in how impact is measured across the organization.

Set the scientific standard for a federated measurement model. Advise partner teams across Amazon on experimental design and analysis, review their methodology, and ensure their results meet a consistent, defensible scientific bar.

Aggregate and report total impact. Synthesize results from both direct experiments and partner-run analyses into a single, trusted view of total impact, and communicate it clearly to senior leadership.

Translate ambiguity into programs. Take open-ended, early-stage measurement problems and shape them into durable programs with clear methodology, reproducible pipelines, and transparent confidence levels.

Influence decision-making at the highest levels. Partner with product, engineering, and business leaders to turn measurement insights into prioritization decisions on catalog quality investments across hundreds of initiatives.

Raise the bar on rigor and transparency. Champion reproducibility, clearly-stated assumptions, and honest reporting of uncertainty (including confidence intervals) so the measurement program becomes the standard the whole company trusts.

A day in the life
Design an experiment. Scope a controlled experiment to quantify how a product-data improvement affects customer and business outcomes — defining the hypothesis, treatment, randomization unit, and sample size.

Model and analyze. Pull large catalog and behavioral datasets, build and refine causal-inference models, and stress-test results for robustness and transparency.

Advise partner teams. Review a partner team's experiment design, flag methodological risks, and help them meet the scientific bar before launch.

Report impact that leaders act on. Interpret results, quantify uncertainty honestly, and fold your findings and partner analyses into the consolidated view of total impact leadership relies on.

Raise the bar. Partner with product and business leaders to turn a measurement insight into a catalog-quality investment decision, and share reusable methods that level up teams across the organization.

Basic Qualifications

- PhD in economics or equivalent
- Experience working with data and leveraging analytics to make decisions
- Experience with the full range of research methods: qualitative and quantitative, attitudinal and behavioral, with deep expertise in one or more areas
- Experience working with technology teams and solving operational issues using technology
- Experience in influencing senior leadership through data driven insights

Preferred Qualifications

- Knowledge of machine learning approaches and algorithms
- Experience with training and deploying machine learning systems to solve large-scale optimizations, or experience in development or technical support
- Knowledge of a scripting language (Python, R, etc.)

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

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. 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.