Amazon Quantitative Analyst candidates report an online assessment, a hiring manager conversation and a multi-interviewer loop dominated by leadership-principle STAR questions, with lighter Excel, forecasting and finance questions.
$102K
Avg. Base Comp
$140K
Avg. Total Comp
Not reported
Typical Rounds
Not reported
Process Length
Across the Quantitative Analyst reports, the dominant theme is Amazon leadership principles asked in STAR format, not heavy coding or statistics. Candidates describe questions about deciding with limited information, acting fast, handling a plan that went wrong, and the most complex issue they had worked on. Several say interviewers pushed into follow-ups about specifics, so thin stories did not hold up. One candidate was also asked to explain why they left each previous job.
The analytical content is real but usually applied. One candidate worked through an Excel case study that tested basic formulas. Another reports about 15 data and forecasting questions mixed into a five-interviewer loop. Others describe ranking vendors to deliver a product, talking through a risk assessment experience, and prioritizing tasks by customer satisfaction, speed and order volume. One candidate sensed that practical finance experience mattered and that an Excel-only background drew skepticism.
Process shape varies. Reports include an online assessment, which in one case simulated an inbox of prioritization decisions. They also include a recruiter or hiring manager screen and a loop of four or more back-to-back interviews. One report describes a business write-up. Another describes repeated rescheduling that led the candidate to withdraw. Outcomes are mixed, including offers, rejections and one role filled before the final loop, and the reports suggest interviewer style varies.
Before interviewing, prepare a bank of detailed STAR stories mapped to specific principles. Make sure each includes the data, the assumptions and the measurable result. Brush up on Excel and practice prioritization and vendor-ranking scenarios. Be ready to explain your forecasting and modeling choices in plain business terms.
Synthesized from 20 candidate reports by our editorial team.
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Real interview reports from people who went through the Amazon process.
I interviewed for the Amazon Economist intern role in March 2026. Two rounds, and each one had the same structure: a causal inference case study followed by a leadership/behavioral question.
The causal inference cases were pretty advanced for an intern role. One of them was: how do you measure the effect of rolling out AI tools that help hiring managers write job descriptions on the quality of candidates hired? The conversation started with pure experiment design, then went deeper into difference-in-differences, and then into modern DiD methods (think staggered adoption, Callaway-Sant'Anna type approaches). It was a real progression — they weren't just checking if you knew DiD existed, they pushed into the frontier stuff.
I don't remember the specific case study from the second round.
For the behavioral questions, one was: "Tell me about a time when you had to disagree with your senior." Classic Amazon disagree-and-commit territory.
The causal inference questions escalated fast. Starting from experiment design and ending at modern DiD methods in a single conversation is a real depth check — make sure you can go all the way through staggered adoption and its assumptions, not just the classic 2x2 DiD setup.
Prep tip from this candidate
The causal inference cases go deep — one question started with experiment design, moved into diff-in-diff, and then pushed into modern DiD methods like staggered adoption. Make sure you can discuss Callaway-Sant'Anna or similar approaches, not just the classic setup. Also prep a strong 'disagree with a senior' story for the behavioral portion.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
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| Question | |
|---|---|
| 2nd Highest Salary | |
| Rolling Bank Transactions | |
| Customer Orders | |
| Comments Histogram | |
| Closest SAT Scores | |
| Subscription Overlap | |
| Top Three Salaries | |
| Monthly Customer Report | |
| Upsell Transactions | |
| Merge Sorted Lists | |
| Jars and Coins | |
| Compute Deviation | |
| Download Facts | |
| Bagging vs Boosting | |
| Average Quantity | |
| Random SQL Sample | |
| Manager Team Sizes | |
| Variable Error | |
| Month Over Month | |
| Compute Variance | |
| Real-Time Transaction Streaming | |
| Flight Records | |
| Prime to N | |
| Paired Products | |
| Google Maps Improvement | |
| N Dice | |
| Top 3 Users | |
| Hundreds of Hypotheses | |
| Recurring Character |
Synthesized from candidate reports. Individual experiences may vary.
Candidates report starting with an online application followed by an online assessment, described as about an hour to an hour and a half. Reported formats include a work simulation with inbox-style scenarios, behavioral questions, and a short logic or personality component. Formats may vary by team.
Some candidates describe recruiter outreach or an HR conversation that explains next steps. One candidate also reports practical questions about salary expectations and whether they could work five days in the office.
Candidates report a phone or video conversation with the hiring manager that is largely STAR-based and tied to leadership principles. Reported prompts include delivering results without enough data or time and describing the most complex issue they had worked on. Follow-up questions typically dig into specifics.
Reports describe a loop of four or more interviewers, in one case a five-interviewer loop, with some candidates reporting back-to-back sessions of 45 to 60 minutes. Leadership principles dominate. One candidate reports about 15 data and forecasting questions mixed in. Other reports describe an Excel case study and vendor ranking or risk assessment discussions.
Outcomes in the reports are mixed: offers, rejections, one role filled before the final loop, and one withdrawal after repeated rescheduling. One candidate found communication fast after each step, while others describe slow or inconsistent timelines, so plan for some uncertainty.
