Walmart Data Scientist interviews typically run 3–5 rounds: coding screen, ML technical rounds, SQL, business case, and a hiring manager or director round. The process spans 1–3 months and is notably ML-heavy with strong emphasis on applied statistics over pure coding.
$122K
Avg. Base Comp
$260K
Avg. Total Comp
3-5
Typical Rounds
6-12 weeks
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 | |
|---|---|
| Customer Orders | |
| Last Transaction | |
| Scrambled Tickets | |
| Cumulative Sales Since Last Restocking | |
| Rejection Reason | |
| Order Addresses | |
| Reducing Error Margin | |
| Using R Squared | |
| P-value to a Layman | |
| Over 100 Dollars | |
| Seasonal Product Performance Analysis | |
| Bagging vs Boosting | |
| Lasso vs Ridge | |
| Assumptions of Linear Regression | |
| Black Friday Shopping Spree | |
| Bias - Variance Tradeoff and Class Imbalance in Finance | |
| Random Forest Explanation | |
| Type I and II Errors | |
| Longest Increasing Subsequence | |
| Bias vs. Variance Tradeoff | |
| Overfit Avoidance | |
| Multicollinearity in Regression | |
| Normal Distribution Sample | |
| Logistic Regression from Scratch | |
| Why Do You Want to Work With Us | |
| k-Means from Scratch | |
| Maximal Substring | |
| Underpricing Algorithm | |
| Your Strengths and Weaknesses |
Synthesized from candidate reports. Individual experiences may vary.
Initial contact typically comes through a referral or direct application. Recruiter communication can be inconsistent, with limited instructions provided upfront about the process.
An early-stage conversation with the hiring manager covering background, motivation, and sometimes behavioral questions like conflict resolution. The tone and structure can vary significantly by team.
A structured first technical round covering core data science fundamentals including basic ML concepts, SQL joins, MLOps, cloud deployment, and a brief Python coding task focused on preprocessing. Questions tend to follow a fixed checklist format.
Deep dive into machine learning concepts such as XGBoost, random forest, Gini impurity, bias-variance tradeoff, and deep learning fundamentals. Some roles also include questions on NLP, LLMs, and agentic AI.
Practical SQL assessment covering table joins, extracting insights, and window functions such as lag and lead. Generally considered to be on the easier side but requires solid working knowledge.
Focuses on product metrics, experimentation, A/B testing, and applying data science to real business contexts such as customer modeling, marketing attribution (MTA), or optimization problems.
For senior and staff-level roles, one or more design rounds assess how candidates approach building real ML systems, such as a catalogue policy violation detector or a text review summarizer. Each round is contingent on positive feedback from the previous one.
A final round with a director-level stakeholder evaluating strategic thinking, communication, and overall fit. This round may include behavioral questions and high-level discussion of past projects and impact.