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Historical candidate account: this article preserves one person’s reported experience. The interview date was not recorded in the original text, and updating this page does not make the account a current hiring process. Names, levels, durations, questions, outcomes, and approximate compensation are as reported, not independently verified or endorsed by the employer. Confirm the current role and assessment with the recruiter.
The following account describes one candidate’s Walmart personalization and recommendation interview.
TLDR
Candidate: Jonny
Group: personalization & recommendation
How it gets started: a friend did an internal referral
Job level: X5
Year of Experience: 5 - 10
Degree: M.S & B.S. in CS
Offer: Yes
TC: ~350K USD
Location: Sunnyvale, CA
Interview process: 4 weeks
Preparation: 2 months
Has a job: yes
Decide to join: N/A
Technical screen round 1:
Basic Python and SQL coding problem
Case study: given a customer's shopping history, predict the next product they will buy.
Technical screen round 2:
Resume deep dive
Case study: predict user's gender given a user's shopping history, names, geo-locations, etc.
Coding: given a set of web pages, implement TF-IDF for a keyword for every webpage.
Virtual Onsite:
Round 1: Machine Learning Concepts
a. How to prevent over-fitting
b. Explain bias vs. variance tradeoff
c. random forest vs. gradient boosted trees, pros and cons
d. How to handle unbalanced samples
Round 2: Coding
Python: kth largest element in an unsorted array
SQL: session stitching
Round 3: ML modeling
Build an ML model to identify users who might be interested in buying product A, what features to use, how to handle categorical variables.
Which algorithm to use, and how to evaluate its performance.
Round 4. Data Modeling
Design a database, ETL jobs, batch processing with Spark
Round 5. Behavioral questions
Leadership, how do you handle deadlines, conflicts with coworkers.
Offer
Received a verbal offer the following week.
Prepare an evaluation that matches the recommendation task
For next-product prediction, define a prediction time and an outcome window. Build features using information available before that time, use an appropriate time-based split, and compare against a simple popularity baseline. Evaluate candidate coverage, ranking quality, cold-start users and products, and online outcomes separately. A historical question in this account asks about predicting gender from shopping history, names, and location. Reporting that question is not a recommendation to infer sensitive characteristics; a present-day answer should challenge the need, permissions, and harms, and consider a non-sensitive task such as expressed product preferences.
Practice one reported prompt, state the data and assumptions, produce a checkable answer, and explain one limitation. Then change a condition and see whether your reasoning still holds. Use this related practice resource for a follow-up; it is separate from the historical interview report.
