How serious Data Scientist candidates prepare for Walmart interviews
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The role of a Walmart Data Scientist
A Walmart Data Scientist plays a crucial role in the company's operations by using data to inform and drive business decisions. They use advanced analytical methods to extract insights from large sets of structured and unstructured data, and use these insights to improve performance and drive growth.
One key responsibility of a Walmart Data Scientist is to help the company understand consumer behavior and preferences. They do this by analyzing data from customer transactions, surveys, and social media, and use this information to inform marketing and sales strategies. They also help optimize pricing and inventory management by analyzing data on sales trends, consumer demand, and supply chain logistics.
Walmart hires for Data Scientist roles across the company and offers different levels of seniority depending on the scope and expected impact of the role. They have opportunities for Senior and Staff level positions, as well as openings for Machine Learning and Computer Vision Engineers. For similar roles, refer to the Google Data Scientist and Tesla Data Scientist guides.
Another important role of a Walmart Data Scientist is to help improve the efficiency and effectiveness of the company's operations. They use data and advanced analytics to identify inefficiencies in the supply chain, and work with other teams to implement solutions. They also use data to improve the performance of the company's website and mobile apps, as well as to inform the design of new products and services.
The examples we’ve outlined above are just a few of the key value-adds that Data Scientists drive at Walmart. There are plenty of others which we haven’t covered here; but we hope this gives you a flavor for what the role could look like.
How to Apply for a Data Scientist Job at Walmart?
Check out Walmart’s career page and browse through the Data Scientist job listings. When you find a role that interests you, be sure to read through the job requirements and qualifications carefully to ensure you meet the criteria. If you have any connections within the company, consider reaching out to them for a referral as it highly increases your chance. When you apply, make sure to tailor your resume to align with the qualifications listed in the job posting. This will help you stand out from other applicants.
And if you need help with customizing your resume specifically for Walmart (or for that matter, any other company), Prepfully provides resume review services by experienced recruiters in your target company that can give you feedback on your resume. It's worth noting that the application process may vary depending on the position and location, and the company may conduct additional assessments or interviews as part of the selection process.
Responsibilities of a Data Scientist at Walmart
The responsibilities of a data scientist at Walmart across roles can broadly be seen as-
- Identifying patterns and trends in complex data sets to inform business decisions.
- Using advanced analytics and statistical techniques to analyze data from multiple sources.
- Use computer vision techniques to create advanced deep learning models for 2D/3D image generation and synthesis, image classification, scene segmentation, object detection, keypoint detection. For instance, they’ve recently hired a bunch of Computer Vision scientists for their AR/VR explorations.
- Perform hands-on modeling and complex analyses using Python, SQL and R.
- Communicating insights and recommendations to stakeholders in a clear and actionable manner.
- Building and maintaining models to support forecasting, prediction, and optimization (for instance, forecasting demand at a product level).
- Continuously monitoring and evaluating the performance of models and data-driven solutions (for instance, in their recommender engine; their sorting engine etc).
Skills and Qualifications needed for Data Scientists at Walmart
Here are some skills and qualifications that will help you excel in your Data Science interviews at Walmart. One thing to note here is that the degree qualification (bachelor’s/ masters’) is different for every role.
- It's beneficial to have at least 5+ years of experience in Data Science roles, which can help you stand out from other candidates. Along with this, having previous work experience where you can demonstrate that you’ve used SQL, Python and/or R can also help your profile.
- Familiarity with advertising, measurement, and digital marketing analytics can often be a major advantage over other candidates.
- Strong coding skills, database knowledge and experience with cloud computing services such as GCP, AWS, or Azure is another thing which can help candidates stand out.
- Understanding of causal inferencing, multi-variate testing & design, A/B testing & design, descriptive analytics, and regression analysis is very important for some Data Scientist roles.
- Experience with big data technologies such as Hive, Hadoop, PySpark, BigQuery and modern data visualization tools like Tableau, ThoughtSpot, Looker, PowerBI, etc. can be an advantage.
- Knowledge of Machine Learning and Deep Learning libraries like scikit-learn, pytorch, tensorflow, numpy is also something we’ve seen some roles explicitly call out in their requirements.
- Experience in statistical methods and advanced modeling techniques (e.g. - SVM, Random Forest, Bayesian inference, graph models, NLP, Computer Vision, neural networks, etc.) along with the optimization and Operational Research techniques can be an added value.
It's important to keep in mind that this list is not exhaustive, and the requirements and qualifications may vary depending on the position and location. It's always best to check the job description and requirements on Walmart's Career page before you apply for the role.
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Walmart Data Scientist Interview Guide
The Walmart data scientist interview has one stage that eliminates more candidates than any other: the Karat technical screen. This 60-minute assessment isn't your typical phone screen. You get 30 minutes to solve 3 LeetCode-style coding problems, and you need to solve at least 2 of them completely, with all test cases passing. Partial solutions don't count. Candidates on Prepfully tell us they've seen peers nail the SQL rounds and case studies only to get cut at Karat because they spent 22 minutes on the first problem.
The other thing that sets Walmart apart is SQL depth. Multiple Prepfully candidates mentioned the same pattern: "SQL was tested more rigorously than I expected." This isn't analyst-level SQL. Walmart expects window functions, CTEs, query optimization, and the ability to explain performance considerations, all under time pressure.
The Walmart data scientist interview process runs 2-5 weeks from application to offer. Most positions require relocating to Bentonville, Arkansas or working hybrid from Sunnyvale, California. The Bentonville requirement has become a major decision point for candidates, many coastal engineers decline offers when they realize remote-only isn't an option.
The Walmart Data Scientist Interview Process
Walmart follows a structured 4-5 stage process. Unlike other tech companies, Walmart moves quickly to the technical gatekeeper (Karat) after a brief recruiter screen.
Relevant Guides
Round 1: Recruiter Screen (30 minutes)
Overview
Quick check on your background, interest level, and salary expectations. The recruiter will ask about your data science experience, why Walmart, and what you're looking for in your next role. Walmart doesn't do long recruiter calls, this is a 20-30 minute conversation that moves quickly to scheduling the Karat assessment.
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Round 2: Karat Technical Screen, The Gatekeeper
Overview
Most candidates fail at this stage in the Walmart data scientist interview. Karat is a third-party interviewing service that Walmart uses to screen candidates before investing team time. The exact structure candidates report on Prepfully:
First 5 minutes: Interviewer introduction and process explanation
Next 2 minutes: Your quick background summary
Section 1, Coding (30 minutes): You face 3 LeetCode-style problems ranging from Easy to Medium difficulty. The brutal requirement: you must solve at least 2 out of 3 completely, with all test cases passing. Partial solutions don't advance you. The interviewer won't let you move to the next question until your code passes all tests.
Common topics from candidate reports: arrays and hashmaps, string manipulation, linked lists basics, graph traversal (BFS/DFS), and dynamic programming fundamentals.
Time management is everything. Candidates who spend more than 20 minutes on the first problem typically don't finish. The pattern that works: 10-15 minutes on Problem 1 (Easy), 15 minutes on Problem 2 (Medium), whatever remains for Problem 3.
Section 2, Math & Probability (10 minutes): Five questions with a 2-minute maximum each. Topics include conditional probability, Bayes' theorem, basic statistics (distributions, variance), and probability theory. These are multiple choice or short answer format.
Section 3, Machine Learning (10 minutes): Two questions with 2 minutes each, though Prepfully candidates told us this section often feels rushed. Focus is on practical ML application
rather than deep theory. Sample questions from Prepfully debriefs: "When would you use Decision Trees vs Random Forest?" or "How do you handle class imbalance in classification problems?"
Success criteria from candidate experiences: Solve 2/3 coding problems completely + demonstrate reasonable understanding in stats/ML sections. Saying "I don't know" to basic ML questions is a red flag, especially for senior roles.
Common failure patterns: Spending too long on the first coding problem, getting stuck on edge cases when core logic works, not testing code thoroughly before submission, and poor time management across the three sections.
The Karat technical assessment format differs significantly from traditional data science interviews. For comparison, the Amazon Data Scientist interview guide follows a more conventional phone screen approach without the strict time-boxed sections.
For comprehensive interview prep check Prepfully's Data Science Interview Course, offers 14 hours of comprehensive prep covering analytics cases, ML system design, statistics, and experimentation, taught by coaches from Meta and other top tech companies.
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Round 3: Technical Interview Rounds (2-3 rounds, 45-60 minutes each)
Overview
SQL Deep Dive: Expect medium-to-hard SQL problems focusing on window functions (ROW_NUMBER, RANK, PERCENTILE_CONT), CTEs, and query optimization. One question reported on Prepfully: "Based on users' most recent transaction date, retrieve users along with the number of products they bought, sorted chronologically."
From Prepfully candidate feedback: "My SQL wasn't wrong, but it was inefficient, they wanted optimization thinking." Interviewers evaluate whether you handle NULLs properly, consider query performance, and explain optimization strategies.
Python/Pandas Coding: Data manipulation focus, not pure algorithms. Sample problems include merging employee-manager tables, implementing TF-IDF from scratch, or cleaning datasets with missing values and outliers.
ML Concepts: Algorithm selection, evaluation metrics, and feature engineering for retail scenarios. High-frequency question from candidate reports: "Explain Random Forest, how does it generate the forest and why use it over other algorithms?"
Check out video guide that delves into the interview process and provides valuable tips tailored to each round of the interview.
Round 4: Business Case Studies
Overview
Walmart emphasizes retail-specific cases. One documented case from Prepfully debriefs: "You discover the pricing algorithm is vastly underpricing a certain product. What steps do you take to diagnose the problem?"
Expected approach: data investigation (timing, scope, magnitude), root cause analysis (data quality, model assumptions), feature importance analysis, validation plan (A/B testing), and long-term solutions (retraining, automated alerts).
Other common cases: demand forecasting for multiple stores with weather and promotional data, customer churn prediction for Walmart+, inventory optimization, and recommendation system design.
Interviewers evaluate problem structure, business acumen, technical depth, and communication clarity.
Round 5: Final Behavioral/Executive Round (45-60 minutes)
Overview
Mix of technical validation and behavioral assessment. Walmart uses the STAR method heavily and looks for alignment with "Save Money. Live Better."
Common Walmart data scientist interview questions from this round: "Tell me about a time you influenced business decisions with data," "How do you explain technical concepts to non-technical stakeholders?" and "Describe working with messy or incomplete data."
Cross-functional collaboration matters. Your examples should demonstrate you can navigate different stakeholder priorities across retail operations, marketing, supply chain, and tech teams.
What Makes Walmart Different: Details Candidates Encounter
The SQL Bar is Higher
Candidates consistently tell Prepfully that SQL testing exceeded their expectations. One DS3 candidate mentioned: "I thought I was solid at SQL, but they went deep on window functions and query optimization in ways I hadn't prepared for."
Walmart expects you to know window functions cold, not only ROW_NUMBER and RANK, but PERCENTILE_CONT, LAG/LEAD, and complex frame specifications. Query optimization matters because Walmart operates at massive scale (petabytes of data, billions of transactions). Binwei Yang, Data Scientist at Walmart Global Tech, has written about managing hundreds of millions of data points across Walmart's infrastructure, highlighting why scale and efficiency dominate technical decisions.
Practice recommendation from successful candidates: solve 50-100 SQL problems of varying difficulty before your Walmart data scientist interview. Focus heavily on StrataScratch (which has Walmart-specific questions) and DataLemur for retail analytics scenarios.
Retail Domain Knowledge Matters
Interviewers expect curiosity about retail even if you haven't worked in the industry. Key metrics: Customer Lifetime Value (CLV), inventory turnover, same-store sales growth, conversion rate, and basket size analysis.
Supply chain concepts that appeared in interviews: cross-docking, vendor-managed inventory (Walmart's Retail Link system), just-in-time replenishment, and RFID tracking.
From candidate feedback on Prepfully: "Even if you're not from retail, study Walmart's business model. They expect you to speak their language." Similar domain expectations appear in other retail and logistics companies. The DoorDash Data Scientist interview guide covers comparable retail analytics frameworks for demand forecasting and operational optimization.
Business Case Over Pure ML Research
Set realistic expectations. Prepfully candidates who accepted offers mention a pattern: "More operational than cutting-edge research."
The strategic versus tactical split skews operational, roughly 30% strategic (innovative projects) and 70% tactical (quick wins, maintenance). One former candidate mentioned to Prepfully: "Less cutting-edge than I hoped for, more operational." Swati Kirti, Group Director of Data Science at Walmart Global Tech, has discussed this balance in public forums, emphasizing how Walmart focuses on practical business impact while maintaining investments in advanced ML capabilities.
Data engineering versus modeling time is closer to 50/50, with significant time on data pipelines, cleaning, and stakeholder management.
Real Walmart Data Scientist Interview Questions
Prepfully candidates reported these specific Walmart data scientist interview questions across recent interviews:
SQL Questions Candidates Reported
Walmart User Transactions Histogram (Medium difficulty, appears frequently): "Based on users' most recent transaction date, write a query retrieving users along with the number of products they bought, sorted chronologically." This tests window functions (ROW_NUMBER to identify most recent), GROUP BY aggregation, and proper date handling.
12-Month Rolling Average by Category (Medium-Hard): "Compute a 12-month rolling average of net sales for every product category." Requires understanding AVG() OVER with proper frame specification (ROWS BETWEEN 11 PRECEDING AND CURRENT ROW), handling missing months, and efficient partitioning.
First-Purchase Cohort Analysis (Hard): "Build a cohort table showing, for each signup month, what percentage of users make repeat purchases in months 1, 2, and 3 after their first order." Tests ROW_NUMBER for isolating first orders, date arithmetic, and cohort framing logic.
Python/Pandas Questions
These Walmart data scientist interview questions focus on practical data manipulation:
Employee-Manager Salary Comparison: "Find employees who earn more than their managers." Tests self-join logic in pandas, merge operations, conditional filtering, and proper handling of the hierarchical relationship.
TF-IDF Implementation from Scratch: "Given a set of web pages, implement TF-IDF for keywords across all webpages." Evaluates understanding of text processing, term frequency calculation, inverse document frequency, and matrix operations with pandas.
ML and Statistics Questions
Random Forest Algorithm (appears in nearly every interview): "What is Random Forest? How does it generate the forest? Why use it over other algorithms?" Interviewers want to hear about ensemble approach, bootstrap sampling, feature randomness, bias-variance tradeoff, and when Random Forest outperforms single decision trees.
Handling Class Imbalance (high frequency): "How do you handle class imbalance in classification?" Strong answers cover SMOTE (Synthetic Minority Oversampling), class weights, threshold adjustment, and business context for retail scenarios (fraud detection is 99.9% legitimate, churn prediction has 95% retention).
Dice Rolling Expected Value (real probability question): "You roll a die and earn whatever face you get. You have a chance to roll a second die but forfeit first earnings. When should you roll again?" Tests expected value calculation (3.5), decision theory, and threshold optimization (roll again if first roll ≤ 3).
A/B Testing Design: "Design an A/B test to evaluate a new recommendation algorithm's impact on conversion rates." Expect to cover hypothesis formulation, success metrics (primary and secondary), sample size calculation (power analysis), randomization strategy, duration determination (accounting for day-of-week effects), and statistical test selection.
Get feedback on your preparation with mock interviews from Prepfully coaches who've worked at Walmart and other retail tech companies.
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Recommended Timeline
Entry-level (P1-P2): 3-4 weeks minimum Mid-to-senior (DS3, P4+): 6-8 weeks recommended
Week-by-week breakdown:
- Weeks 1-2: SQL intensive, 5-10 problems daily on window functions, CTEs, optimization
- Weeks 3-4: ML algorithms with retail context and feature engineering
- Weeks 5-6: Retail-specific case studies (2-3 per week)
- Weeks 7-8: Mock interviews and STAR story practice
What to Prioritize
SQL is number one. The SQL bar at Walmart exceeds typical data science roles. Focus on window functions, CTEs, and query performance optimization. Solve 50-100 SQL problems minimum.
Karat time management. Practice solving 2 LeetCode Medium problems in 30 minutes total. Time management under pressure is a distinct skill from untimed problem-solving.
Retail-specific case studies. Study Walmart's actual business challenges: cross-docking, inventory turnover, promotional pricing, and supply chain efficiency. Read Walmart's annual reports.
STAR stories with numbers. Have 6-8 behavioral stories with quantified impact: "Reduced forecast error by 20%, saving $1B annually" or "Improved model accuracy from 73% to 89%, increasing conversion rate by 12%."
Mock interviews help significantly, Prepfully candidates reported time pressure simulation and getting pushback on approaches helped more than solo practice.
Walmart DS Compensation overview
Walmart uses a P-level system (P1 through P6) for individual contributors. Verified compensation data from Levels.fyi based on employee submissions:
P1 (Entry-Level Data Scientist): Total compensation $129K ($127K base, minimal to no stock initially, $1.7K bonus). Typical profile: Master's degree with 0-2 years industry experience or Bachelor's with 3-5 years analytics experience.
P2 (Data Scientist II): Total compensation $183K ($137K base, $22.8K RSUs, $23.3K bonus). Requires 2-4 years of data science experience or recent PhD graduate.
P4 (Senior Data Scientist): Total compensation $237K ($173K base, $42.8K stock, $21K bonus). Generally 5-8 years of experience with demonstrated project leadership and mentoring.
P5 (Staff Data Scientist): Total compensation $280K ($200K+ base, $60K+ stock, $30K+ bonus). Requires 10+ years with technical leadership and strategic initiative ownership.
Location Makes a Big Difference
Bentonville, Arkansas (headquarters): Lower end of salary ranges, but cost of living is 40-50% lower than coastal cities. The calculation Prepfully candidates mention: "$200K in Bentonville goes further than $280K in Bay Area after taxes and housing."
Sunnyvale, California (Silicon Valley tech hub): 20-30% higher total compensation than Bentonville for the same level. Senior DS roles reach $234K base in Sunnyvale versus $180K in Bentonville.
Stock vesting: Walmart uses 4-year vesting for most levels (25% annually, no cliff) or 3-year vesting for some roles (33% annually). Some positions offer quarterly vesting (8.25% every 3 months).
Negotiation insights: Level assignment has the biggest impact on total compensation, much more than negotiating base salary within a level. Sign-on bonuses show the most flexibility, ranging from $10K-$50K. Base salary is relatively rigid within level bands.
The Walmart DS Interview tl;dr
Master the Karat interview first. Most candidates fail at this gatekeeper stage in the Walmart data scientist interview process. You need to practice solving 2 LeetCode Medium problems in 30 minutes total, not 30 minutes each. Time management under pressure is a distinct skill from problem-solving ability.
SQL depth matters more than you think. Plan to solve 50-100 SQL practice problems minimum before interviewing. Focus specifically on window functions (ROW_NUMBER, RANK, PERCENTILE_CONT, LAG/LEAD), CTEs for complex queries, and query optimization concepts. Walmart's SQL bar exceeds typical data science roles.
Retail case studies require domain preparation. Generic tech company cases won't prepare you properly. Study Walmart's actual business challenges: demand forecasting with seasonal patterns, pricing optimization while maintaining "Everyday Low Price" positioning, inventory management balancing stockouts versus overstock, and supply chain efficiency at massive scale.
Prepfully's Data Science Interview Course covers retail-specific frameworks for analytics and ML cases.
Behavioral assessment carries significant weight. Have 6-8 STAR stories prepared with quantified business impact. Walmart interviewers want specific metrics showing how your work influenced decisions, not vague descriptions of team projects. The "Save Money. Live Better" mission should resonate in your examples.
Location decision is critical. Bentonville versus Sunnyvale isn't only a compensation difference, it's a lifestyle choice. Limited remote flexibility means you need to seriously evaluate whether Bentonville fits your life plans. The cost of living advantage is substantial, but so is the geographic isolation.
Manage expectations about the work. This role involves more operational improvements than cutting-edge research, roughly 50/50 split between modeling and data engineering work, significant stakeholder management and communication time, and focus on business impact over novel techniques. Set realistic expectations to avoid disappointment.