Amazon Economist Job Interview

Historical candidate account: this article preserves one person’s reported experience. The interview date was not recorded in the original text, and updating this page does not make the account a current hiring process. Names, levels, durations, questions, outcomes, and approximate compensation are as reported, not independently verified or endorsed by the employer. Confirm the current role and assessment with the recruiter.

Here is what the Amazon Economist interview process looked like.

Amazon Economist Job Interview Case Study Summary

Candidate: Lisa

How it gets started: a friend gave an internal referral.

Job Level: Entry

Year of Experience: recent college graduate

Degree: M.S. in behavioral science

Offer: Yes

TC: ~200K USD

Location: Seattle WA

Interview process: 3 months

Preparation: 4 months

Has a job: no

Decide to join: N/A

Amazon Economist Job Interview Round 1: Recruiter Phone Call 

1. Logistics questions: if VISA sponsorship will be needed; how soon can she get started, relocation.
2. Asked to choose one of the 3 economist job tracks at Amazon:
   1. Forecasting/ Time Series analysis; (CHOSEN)
   2. Causal analysis;
   3. Empirical IO (industrial organization)

Amazon Economist Job Interview Round 2: 

1. Leadership Principles: 
   1. Tell me about a time you had a conflict with someone, how did you handle it, what did you learn?
   2. Give me an example of a task/project that you did above and beyond.
   3. Tell me about a failure. What would you have done differently?
2. How do we forecast amazon's website traffic? Could you walk me through the process?

Amazon Economist Job Interview Virtual Onsite Interview:

Round 1: SQL Coding (see more amazon SQL interview questions on sqlpad.io)

1. Given driver_id, city, timestamp, report the number of drivers by the hour of the day/city for a ridesharing app;
2. How to create a model to predict the number of drivers by city/hour of the day.

Round 2: Time Series Analysis/Forecasting Modeling

1. How do you verify if the time series data is stationary, theoretically and practically?
2. What kind of problems/issues will non-stationary data cause?
3. How do we fix the non-stationary data and fit them with a stationary model?
4. How do you usually select features for your model?

Round 3. Leadership Principles/Behavioral Questions with a Bar Raiser

1. Tell me about a time you disagreed with your boss/manager/advisor.
2. Tell me about a time you worked on a project with a tight timeline.
3. What is your biggest weakness?

Round 4: Leadership Principles/Behavioral Questions with the Hiring Manager

1. What do you see yourself in 5 years?
2. Why do you want to join Amazon?
3. Tell me an example you did an end-to-end model, including data modeling, ETL, model refresh.
4. Do you have any questions for me?

Offer

Lisa received the job offer after a month of the virtual onsite interview.

Prepare a forecasting problem from observation to decision

For drivers by city and hour, define whether the quantity is distinct observed drivers, active supply, or completed trips. State the timezone and how repeated events are deduplicated. Forecast using only information available at each prediction time, evaluate with a rolling-origin design, and compare with a seasonal baseline. Assess nonstationarity in the context of the model; not every forecasting method requires transforming the raw series to stationarity. Report horizon-specific errors and uncertainty alongside the operational decision.

Practice one reported prompt, state the data and assumptions, produce a checkable answer, and explain one limitation. Then change a condition and see whether your reasoning still holds. Use this related practice resource for a follow-up; it is separate from the historical interview report.