The first round was behavioral questions and then a case study. The second round followed a similar format but with different behavioral questions and a slightly harder case study. After, there is a team matching stage.
Standard recruiter screen and hiring manager call, deep dive into both behavioral and causal inference methods (case studies). No coding element. Case studies are deep & technical, what methods would you use (experimentation/DiD..) and assumptions and caveats...etc
Questions d'entretien [1]
Question 1
If we were doing a promotion/change in a given region/set of customers, how would we test this impact
Interviewed for an Economist role at Amazon. The process was structured and data-focused, with a strong emphasis on applied econometrics, causal inference, and practical problem-solving.
The interview consisted of multiple rounds, including an initial screening followed by technical interviews. These covered topics such as regression analysis, experimental design, and identification strategies. I was asked to discuss past projects in detail, explain methodological choices, and interpret results clearly. There was also a focus on translating complex analyses into business-relevant insights.
Some questions were theoretical, but most were applied and required thinking through real-world scenarios. The interviewers were knowledgeable and professional, and the overall experience was rigorous but fair.
Overall, the process reflects Amazon’s focus on analytical rigor and the ability to connect economic reasoning with business impact.
Questions d'entretien [1]
Question 1
Explain how I would estimate the causal effect of a policy/intervention when randomized experiments are not feasible, including discussion of identification strategies (e.g., difference-in-differences, instrumental variables, and selection issues).
Walk through how I would design and analyze an A/B test, including power calculations, potential biases, and interpretation of results.
Discuss how to handle selection bias and omitted variable bias in observational data.
Interpret regression outputs and explain what coefficients mean in a business context.
Describe a past project in detail, including data, model choice, assumptions, and limitations.
