PhD Econ Intern, Store Economics and Science
Possiede le competenze, l'esperienza e la determinazione necessarie per avere successo in questo ruolo? Lo scopra qui sotto.
We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to apply their causal inference / structural econometrics skillsets to solve real world problems. The intern will work in the area of Store Economics and Science (SEAS) and develop models to SEAS.
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.
Key Job Responsibilities
Causal inference: The core of the project requires the intern to apply rigorous experimental and quasi‐experimental methods (e.g., DiD, matching, IV, or RD) to identify the causal relationship between customer demographic characteristics and upper‐funnel shopping behavior. The intern will need to clearly articulate identification assumptions, conduct robustness checks, and demonstrate depth in at least one method — for example, by addressing heterogeneous treatment effects across customer segments or applying modern extensions such as staggered DiD or doubly robust estimation.
Experiment design: The experiment design deliverable will demonstrate the intern's ability to translate observational findings into a testable hypothesis with a well‐specified randomized design, including power analysis, proper randomization strategy, and pre‐registration of outcomes.
Technical adaptability and LLM proficiency: The project requires the intern to quickly learn and leverage LLM‐based pipelines. The intern needs an entry‐level understanding of large language models and will work hands‐on with the most advanced LLM models used internally at Amazon. This demonstrates the intern's ability to operate at the intersection of AI and economics — working independently with large‐scale databases using Python and SQL while understanding how LLM outputs feed into economic analysis.
Communication: The insight report, experiment design, and grocery business proposal will demonstrate the intern's ability to present technical economic ideas clearly to a business audience — translating causal estimates and statistical findings into actionable recommendations that non‐technical stakeholders can understand and act on.
A Day in the Life
The internal stakeholders are leadership of SEAS and CBA. The intern is expected to meet weekly with mentor and manager to discuss progress and have a final representation to the stakeholder team.
About the Team
The Stores Economics and Science Team (SEAS) is a Stores‐wide interdisciplinary team at Amazon with a peak‐jumping mission focused on disruptive innovation. The team applies science, economics, and engineering expertise to tackle the business's most critical problems, working to move from local to global optima across Amazon Stores operations. SEAS builds partnerships with organizations throughout Amazon Stores to pursue this mission, exploring frontier science while learning from the experience and perspective of others. Their approach involves testing solutions first at a small scale, then aligning more broadly to build scalable solutions that can be implemented across the organization. The team works backwards from customers using their unique scientific expertise to add value, takes on long‐run and high‐risk projects that business teams typically wouldn't pursue, helps teams with kick‐start problems by building practical prototypes, raises the scientific bar at Amazon, and builds and shares software that makes Amazon more productive.
Basic Qualifications
* Are enrolled in a Ph.D. in econometrics, statistics, industrial engineering, operations research, optimization, data mining, analytics, or equivalent quantitative field.
Preferred Qualifications
* Strong skills in coding in Python, machine learning application, and causal inference theory.
* Familiarity with large language models and experience working with LLM‐based pipelines.
The starting pay for this position is listed below. Final starting pay will be based on factors including experience, qualifications, and location.
USA, WA, Seattle – 129,200.00 – 174,700.00 USD annually
Starting Day 1 of employment, Amazon offers EAP, Mental Health Support, Medical Advice Line, 401(k) matching. xrdztoy Learn more about our benefits at is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.