Chewy Data Scientist interview typically runs about 6 rounds: recruiter screen, general screening, and a final loop of 4 interviews. It is usually remote and can take several weeks, with no live coding and a collaborative ML case-study style.
$159K
Avg. Base Comp
$279K
Avg. Total Comp
6-7
Typical Rounds
3-5 weeks
Process Length
We've seen Chewy lean much more heavily on applied machine learning judgment than on algorithmic trickery. The strongest signal from candidate experiences is that interviewers want to hear how you frame a business problem, choose the right data, and connect model output to revenue or campaign performance. One candidate described a marketing-revenue case where the conversation kept expanding as new details were introduced, which suggests the team is evaluating whether you can reason through ambiguity in a way that stays grounded in the business.
A recurring theme is that Chewy seems to care about whether you can make practical tradeoffs, not just name the fanciest model. Candidates reported questions around marketing efficiency, email and banner ad strategy, and even explaining random forests in plain language. That mix tells us they are listening for clear model selection logic and a credible measurement plan more than for textbook definitions. If your answer jumps straight to tooling without explaining why a campaign should be compared, how success should be measured, or what data would actually move the decision, you will likely feel the room go flat.
We also notice a consistent emphasis on collaboration and communication. Multiple candidates mentioned behavioral conversations about conflict, teamwork, and culture fit, alongside a remote format where the interviewer would actively feed more information into the case. That pattern matters: Chewy appears to value people who can think out loud, adapt as the problem changes, and stay structured without becoming rigid. The candidates who do best here usually sound like someone the business could hand a messy growth question to and trust the answer would be both thoughtful and usable.
Synthesized from 1 candidate report by our editorial team.
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Real interview reports from people who went through the Chewy process.
I went through a fairly extensive interview process at Chewy, totaling around six rounds. It started with a recruiter screen, followed by a general screening interview, and then moved into a final round loop consisting of four additional interviews (with possibly one more on top of that).
What stood out about Chewy was that there were no live coding questions. Instead, the technical portion was more of an open-ended, in-depth machine learning discussion. The key question I was given was something like: 'You're in charge of a marketing campaign and we're trying to maximize revenue — how would you approach this? How do you select between different campaigns? What data would you use, what models would you use, and how would you measure success?' As I worked through the problem, the interviewer would feed me more information, making it feel like a collaborative, end-to-end ML case study rather than a rigid Q&A.
All interviews were conducted remotely, and the coding (where applicable) was done via a shared notepad rather than a dedicated code execution environment. The process also included behavioral rounds focused on culture fit and background — things like how you handle conflict and how you work within a team.
Prep tip from this candidate
Prepare to walk through a full end-to-end ML case study verbally — practice structuring open-ended business problems (like campaign optimization) by clearly articulating data sourcing, model selection, and success metrics, since the interviewer will progressively add constraints rather than ask discrete questions. Expect no live code execution, so be ready to write pseudocode or sketch logic in a plain shared notepad while keeping your reasoning narrative front and center.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Chewy
Select the 2nd highest salary in the engineering department
| 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 | |
| Hurdles In Data Projects | |
| Valid Anagram | |
| Banner Ad Strategy Success | |
| Digital Marketing Metrics | |
| Find Mismatched Words | |
| String Palindromes | |
| Client Solution Pushback | |
| Why Do You Want to Work With Us | |
| Generative AI Privacy | |
| Weighted Average Sales | |
| Marketing Dollar Efficiency | |
| Identical Pen Pricing | |
| Direct Mail | |
| Email Marketing System | |
| Empty Neighborhoods | |
| Rolling Bank Transactions | |
| Monthly Customer Report | |
| Comments Histogram | |
| Closest SAT Scores | |
| Subscription Overlap |
Synthesized from candidate reports. Individual experiences may vary.
An initial conversation with recruiting to review your background, role fit, and overall interest in the Data Scientist position. This appears to be the first step before moving into the technical screening process.
A broad screening interview that serves as the first substantive evaluation. Based on the experience, this round likely covers your ML background, project experience, and high-level problem solving in an open-ended format.
A remote loop of several interviews focused on machine learning case studies, behavioral fit, and team collaboration. One key discussion centered on how to design a marketing campaign optimization approach: selecting campaigns, choosing data and models, and defining success metrics. There were no live coding questions; any coding was done in a shared notepad, and the interviews were collaborative and exploratory.