Chewy data scientist candidates describe a recruiter screen, a hiring manager conversation, technical discussion or coding, and a multi-interview final loop mixing ML case work, business questions and behavioral rounds.
$160K
Avg. Base Comp
$184K
Avg. Total Comp
6-7 rounds
Typical Rounds
2-8 weeks
Process Length
Chewy's data scientist interviews, as candidates describe them, blend technical depth with business judgment. Accounts differ by team and date, so treat the format as variable rather than fixed.
Several candidates describe a recruiter call followed by a hiring manager conversation. One reports the hiring manager asked three behavioral questions tied to Chewy's operating principles, and another says the hiring manager spent much of the time probing past projects. Be ready to defend project decisions in detail, because follow-up questions came on whatever was mentioned.
The technical content spans several styles. One candidate reports no live coding, only an open-ended ML discussion about maximizing revenue from a marketing campaign, with the interviewer adding information as the answer developed. Another reports Pandas coding plus questions on machine learning, time series and operations research. A third reports a Python, SQL and basic ML assessment, then a four-round virtual onsite with coding, ML system design for recommendations, a debugging scenario about a drop in click-through rate, and a second behavioral round. A fourth reports a team-specific fraud detection case covering data preparation and hyperparameter tuning.
One candidate reports a rushed schedule with four back-to-back meetings on short notice, SQL and Python questions, and an offer later rescinded because of a reorganization. Other candidates reported offers or were still waiting. Prepare to reason aloud about experiments, marketing and recommendation problems, and keep your Pandas and SQL sharp.
Synthesized from 5 candidate reports by our editorial team.
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Real interview reports from people who went through the Chewy process.
I have had 2 rounds, one more pending. The first round was with a senior data scientist. The first half was a resume deep dive, and the second half was him giving me a sample problem and asking me to define my end-to-end data science model approach. He asked a lot of cross questions, nothing to do with operating principles as such.
Questions asked: The case they asked me was very specific to the team I was about to join. It was for detecting fraudulent activity and eliminating occurrences of false negatives. What my hyperparameter tuning would look like and what would be my data preparation steps.
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Topics based on recent interview experiences.
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| Question | |
|---|---|
| Customer Orders | |
| Random SQL Sample | |
| Top 3 Users | |
| Retailer Data Warehouse | |
| Booking Regression | |
| Monthly Product Sales | |
| Max Quantity | |
| Random Forest Explanation | |
| Marketing Channel Metrics | |
| Valid Anagram | |
| Hurdles In Data Projects | |
| Banner Ad Strategy Success | |
| Digital Marketing Metrics | |
| Find Mismatched Words | |
| String Palindromes | |
| Client Solution Pushback | |
| Why Do You Want to Work With Us | |
| Weighted Average Sales | |
| Generative AI Privacy | |
| Marketing Dollar Efficiency | |
| Identical Pen Pricing | |
| Direct Mail | |
| Email Marketing System | |
| Empty Neighborhoods | |
| Subscription Overlap | |
| Rolling Bank Transactions | |
| Monthly Customer Report | |
| Comments Histogram | |
| Closest SAT Scores |
Synthesized from candidate reports. Individual experiences may vary.
Candidates report starting with a recruiter call. One describes a roughly thirty-minute conversation covering background, compensation expectations and interest in Chewy. Another was contacted unexpectedly to discuss availability. Expect logistics and fit questions rather than technical depth.
Candidates report a hiring manager round mixing behavioral and high-level technical topics. One says the manager kept digging into past projects. Another reports three behavioral questions mapped to Chewy's operating principles. It may also include open-ended business questions, so prepare project stories with clear decisions and outcomes.
Formats vary by candidate. One reports a choice between HackerRank and a take-home covering Python, SQL and basic ML. Another reports Pandas coding with ML, time series and operations research questions. A third reports no live coding at all, only an ML discussion. Prepare for either coding or discussion.
Candidates report cases built around business problems: maximizing revenue from marketing campaigns, a team-specific fraud detection problem, and a recommendation system for the homepage, autoship and abandoned cart. Interviewers may add constraints as you go, so state data, models, metrics and tradeoffs clearly.
Candidates report a final loop of about four interviews, possibly with one more. One says it combined coding, ML system design, a production debugging scenario and a behavioral round. Another reports short-notice back-to-back meetings. Expect behavioral questions on conflict and teamwork alongside technical depth.
