Intern - Economics, WW Returns & ReCommerce

Amazon•Bellevue, WA

About The Position

We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to apply their causal inference skillsets to solve real world problems. The intern will work in the area of Returns & ReCommerce and develop methods to validate and contextualize causal inference estimates. Our PhD Economist Internship Program offers hands-on experience in applied economics, supported by mentorship, structured feedback, and professional development. Interns work on real business and research problems, building skills that prepare them for full-time economist roles at Amazon and beyond. You will learn how to build data sets and perform applied econometric analysis collaborating with economists, scientists, and product managers. These skills will translate well into writing applied chapters in your dissertation and provide you with work experience that may help you with placement. These are full-time positions at 40 hours per week, with compensation being awarded on an hourly basis. About the team The Returns & ReCommerce Economics Intelligence (RREI) team brings together economists, data scientists, data analysts, and business intelligence engineers to deliver innovative research and products that discover and surface returns-influencing behavior, trends and their root causes. We leverage a range of scientific approaches such as causal modeling, structural and choice modeling, time series, ML, and optimization models to yield tangible insights targeted at reducing the cost of returns and concessions without slowing down the Amazon flywheel.

Requirements

Candidates are required to be enrolled in an Economics PhD program or closely related field
Applicants should be enrolled in at least the 3rd year of their PhD program AND NOT currently on the job market

Nice To Haves

Experience with machine learning (ML) tools and methods
Methods for validating causal inference estimates (experimental benchmarks, placebo and falsification tests, synthetic data approaches, etc).