Walmart Data Scientist interview questions

13 Data Scientists have shared notes from their Walmart interviews, with the questions they were asked in each round. That is 9% of the 139 Walmart interview notes on Screna AI.

13 Data Scientist interview notes

Walmart·Data Scientist·Hiring Manager Screen

Jul 2026
Casual hiring manager chat for a Data Scientist role at Walmart Labs, mostly focused on getting a read on who you are as a person rather than anything technical.
  • What is the biggest setback you have experienced in your life and how did you overcome it?
  • Which high school did you attend?

“I went with a professional story but it felt a bit sanitized in hindsight.”

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Walmart·Data Scientist·Online Assessment (OA)

Jul 2026
Online assessment for a Data Scientist role, heavy on PostgreSQL window functions and statistical aggregations. Four SQL tasks back to back, each one a bit more painful than the last.
  • Given a multi-section online assessment schema (candidates, submissions, questions, responses), write a query to compute per-subtype accuracy and median response time for each attempt, filtering to attempts with at least 15 verbal responses. Also return total verbal response count and total time per attempt.
  • Flag 'rushing' attempts where more than 20% of verbal responses have a response time under 15 seconds. Return attempt_id, candidate_id, and the rushing rate.
  • Within the verbal section, find the subtype with the strongest absolute Pearson correlation between response time and correctness. Return subtype, correlation value, and sample size n. Use PostgreSQL's built-in corr() function.
  • For each candidate, compute verbal questions per minute and correct answers per minute from their most recent attempt only. Then rank candidates within their location by correct per minute, breaking ties using lower average time per question.

“The median part is what got me.”

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Walmart·Data Scientist·Technical Phone Screen

Jun 2026
Interviewed for a Data Scientist role at Walmart Labs and got a scenario-based stats question about A/B testing for a marketing feature. Pretty technical for what I expected to be a lighter screen.
  • How do you calculate the required sample size for an A/B test?
  • What approaches can you use to increase the statistical power of an experiment without just collecting more data?

“I knew the general answer but fumbled the vocabulary under pressure.”

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Walmart·Data Scientist·Technical Phone Screen

Jun 2026
Had a technical discussion with the ML engineering team at Walmart Labs for a Data Scientist role, centered entirely on cross-validation and model selection. Pretty niche focus for one session but they went deep on it.
  • Can you explain how k-fold cross-validation works and why it helps reduce overfitting?
  • How do you choose the value of k, and what are the trade-offs involved in that choice?
  • What is nested cross-validation and in what situations would you use it over standard cross-validation?

“I walked through the mechanics fine, splitting data into k folds, training on k-1, testing on the held-out one, rotating through.”

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Walmart·Data Scientist·Technical Phone Screen

Jun 2026
Walmart Labs data science interview focused heavily on ad conversion rate prediction, which sounds applied but quickly went into regression theory and ML fundamentals. A few questions I felt okay about, a couple I definitely fumbled on the stats side.
  • How would you build a model to predict ad conversion rate for the next time period using historical campaign data like impressions, clicks, and conversions?
  • Should the conversion rate target variable be transformed before modeling, and if so, why?
  • If you had access to more data, what additional features would you incorporate into the model?
  • How would you evaluate the performance of this conversion rate prediction model?
  • Walk me through the loss function used in logistic regression.
  • Explain PCA in terms of eigenvalues and eigenvectors, and describe the assumptions the method relies on.

“I talked through a few modeling options and landed on using lagged features from prior periods.”

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Walmart·Data Scientist·Technical Phone Screen

Jun 2026
Interviewed for a data scientist role on the growth analytics team at Walmart Labs. The session was heavily focused on causal inference methodology, specifically PSM, which I hadn't drilled as hard as I probably should have.
  • What is Propensity Score Matching and when would you actually use it in a business context?
  • What assumptions need to hold for PSM to give you unbiased causal estimates?
  • After running PSM, how do you check that the matching actually worked? Walk me through your balance diagnostics.

“I knew the textbook definition but stumbled when they pushed for concrete business scenarios.”

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Walmart·Data Scientist·Onsite - System Design / Architecture

Jun 2026
Walmart Labs DS interview that was basically a full ML system design session. They wanted end-to-end coverage on an item categorization system, which sounds scoped until you realize they mean everything from labeling pipelines to retraining triggers.
  • Design a machine learning system that predicts the category of a retail item. Walk through data collection and labeling, feature engineering across text, image, and categorical inputs, model architecture selection, training pipeline, serving strategy, evaluation metrics for multi-class classification, and how you'd handle monitoring and retraining.

“This was the whole interview, not just one question.”

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Walmart·Data Scientist·Technical Phone Screen

May 2026
Got a technical screen for a Data Scientist role at Walmart Labs that was basically one pandas coding question. Pretty straightforward if you know your way around DataFrames, but the data structure they gave you requires a bit of thought before you just start typing.
  • Given a dictionary where each key is a column name and each value is a list of [row_id, cell_value] pairs, write Python code using pandas to produce a wide DataFrame aligned by row_id.

“The structure trips you up for a second because it's not a normal dict-of-lists you can just throw at pd.DataFrame().”

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Walmart·Data Scientist·Technical Phone Screen

May 2026
Interviewed for a Data Scientist role at Walmart Labs and got a technical question about LLM inference that I thought I was prepared for until I actually had to explain it out loud.
  • During LLM inference, the same prompt can produce different outputs each time. Where does that randomness come from? Walk through the role of temperature, top-k and top-p sampling, and how greedy decoding fits into the picture.

“I knew the surface-level answer but fumbled when they pushed on the interaction between temperature and top-p.”

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Walmart·Data Scientist·Technical Phone Screen

May 2026
Walmart Labs data science interview with a string manipulation coding question. Pretty straightforward session, they cared more about whether you could talk through complexity and edge cases than just getting the answer.
  • Given a string, return the index of the first character that doesn't repeat anywhere else in the string. Return -1 if none exists.

“Two passes: count frequencies first, then scan again for the first count-of-one character.”

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