Walmart Data Scientist interviews typically span 3-5 rounds over 1-3 months. The process is ML-heavy and mixes a recruiter screen, technical fundamentals, SQL, business/problem-solving, and sometimes a senior ML system design or leadership round.
$161K
Avg. Base Comp
$260K
Avg. Total Comp
3-5
Typical Rounds
1-3 months
Process Length
We've coached candidates through Walmart's Data Science process across multiple teams — last mile delivery, e-commerce, and international offices — and one pattern stands out clearly: the breadth of the technical bar is wider than most candidates expect, but the depth is uneven. The first screen often feels like a structured checklist — bias-variance tradeoff, SQL join types, MLOps basics — but later rounds can pivot sharply into open-ended ML system design, like building a catalogue policy violation detector or a text review summarizer. Candidates who prepare only for one mode tend to get caught off guard.
A recurring theme across experiences is that the process can stretch significantly without proportional communication. Multiple candidates reported waiting weeks after final rounds with no feedback, following up repeatedly, and ultimately receiving automated rejections. One candidate even had a Director express surprise at the outcome and promise recruiter follow-up — which came a month and a half later. This isn't just an inconvenience; it shapes how you should emotionally and logistically manage the process. Don't treat silence as a signal either way.
On the technical side, we've noticed Walmart interviewers are particularly interested in how you connect model mechanics to business outcomes. Questions around XGBoost versus random forest, Gini impurity, and A/B testing weren't asked in isolation — they came paired with product metrics and experimentation framing. The NLP, LLM, and agentic AI questions one candidate encountered suggest certain teams are actively modernizing their stack and want to see that you're thinking beyond classical ML. Knowing the fundamentals cold is table stakes; being able to reason about them in a retail or delivery context is what actually differentiates candidates here.
Synthetized from 3 candidates reports by our editorial team.
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Real interview reports from people who went through the Walmart process.
The hardest part for me was realizing this wasn’t going to be a pure coding interview. Walmart’s process for the Data Scientist role felt mostly stat-based and optimization-heavy, with a lot of emphasis on machine learning fundamentals and how you’d apply them in business settings. I went through three rounds: a coding round first, then two technical interviews. The coding portion was pretty light compared with what I expected, more in the LeetCode-simple range and Python-based than anything deep or tricky. The technical rounds leaned much more toward core deep learning and machine learning concepts, and I also got questions around NLP, LLMs, and agentic AI, which surprised me a bit. There were also marketing-style questions tied to customer modeling and MTA, so it helped to think about how you’d measure impact in a real product or campaign context rather than just talking theory.
Prep tip from this candidate
Review the basics of deep learning and machine learning thoroughly, and be ready to explain A/B tests and power calculations clearly. I’d also prepare for business-facing questions around customer modeling and marketing attribution, since those came up alongside the technical ML discussion.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Walmart
Write a query that returns all neighborhoods that have 0 users.
| Question | |
|---|---|
| 2nd Highest Salary | |
| Customer Orders | |
| Last Transaction | |
| Top 3 Users | |
| Scrambled Tickets | |
| Cumulative Sales Since Last Restocking | |
| Bagging vs Boosting | |
| Rejection Reason | |
| Reducing Error Margin | |
| Order Addresses | |
| P-value to a Layman | |
| Using R Squared | |
| Over 100 Dollars | |
| Random Forest Explanation | |
| Longest Increasing Subsequence | |
| Seasonal Product Performance Analysis | |
| Lasso vs Ridge | |
| Assumptions of Linear Regression | |
| Black Friday Shopping Spree | |
| Bias - Variance Tradeoff and Class Imbalance in Finance | |
| Type I and II Errors | |
| Bias vs. Variance Tradeoff | |
| Overfit Avoidance | |
| Multicollinearity in Regression | |
| Merchant Dashboard Design | |
| Logistic Regression from Scratch | |
| Normal Distribution Sample | |
| Client Solution Pushback | |
| Why Do You Want to Work With Us |
Synthesized from candidate reports. Individual experiences may vary.
The process usually starts with a recruiter outreach or referral-based contact. This step is often light on detail, and candidates may receive limited upfront guidance on the exact sequence, timing, or team-specific expectations.
An early conversation with the hiring manager typically covers your background, motivation, and fit for the team. Depending on the group, it may also include behavioral prompts such as conflict resolution and discussion of past project impact.
The first technical stage is usually structured and checklist-driven, covering core data science fundamentals such as SQL joins, basic ML concepts, MLOps, cloud deployment, and a short Python preprocessing task. A separate ML-focused round may then probe XGBoost, random forest, Gini impurity, bias-variance tradeoffs, and sometimes NLP or LLM topics.
Candidates are commonly asked to work through practical SQL problems involving joins, window functions like lag and lead, and extracting insights from tables. The round is generally straightforward, but it still requires clean query writing and comfort with common analytical patterns.
This round focuses on applying data science to business problems, with emphasis on product metrics, experimentation, and A/B testing. Candidates may be asked to reason through customer modeling, marketing attribution, or optimization scenarios and connect model choices to measurable outcomes.
For more senior roles, the later stage can include one or more ML system design discussions, such as designing a catalogue policy violation detector or a text review summarizer. A final director-level conversation may also assess strategic thinking, communication, and overall leadership fit.