eBay Data Scientist reports describe Python and SQL screening, technical discussions, resume-led project questions, and case-oriented follow-ups. Prepare to explain query logic, applied modeling choices, and your reasoning clearly.
$187K
Avg. Base Comp
$239K
Avg. Total Comp
3 rounds
Typical Rounds
Not reported
Process Length
Reported eBay Data Scientist interviews emphasize practical Python and SQL work alongside conversations about projects and applied data-science judgment. One candidate described an online CodeSignal round with Python and SQL before later in-person interviews. Another explicitly reported three rounds: Python and SQL; a project discussion with SQL and a case study; then a deeper project discussion involving basic math and GenAI.
SQL is the clearest recurring theme. Reported questions included groupby and aggregation work, a gaps-and-islands problem about finding streaks, and a SQL window-function discussion. Practice explaining how your query works as well as producing a correct result. Be ready to state assumptions, describe the role of partitions or grouping, and defend your approach when an interviewer probes it.
Your resume may guide the later conversation. One candidate was asked about DBSCAN, LDA, GenAI, and the choice between DBSCAN and k-means. Revisit the data, evaluation criteria, tradeoffs, and limitations behind each project you list. For case-oriented discussion, focus on making your analytical reasoning visible: clarify the problem, identify useful data, and explain how your proposed method supports a decision. Format and emphasis can vary by interview team, but the available reports consistently support preparation in SQL, Python, and project communication.
Synthesized from 3 candidate reports by our editorial team.
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Real interview reports from people who went through the Ebay process.
=> 3 Rounds in total
1st round was virtual and the rest were face to face(in office)
Questions asked: Initial round revolved around Python(pandas) and SQL A lot on groupby, and aggregations My resume related stuff => DBSCAN, Gen AI, LDA(Latent dirichlet allocation) Difference b/w DBSCAN and k-means (why did you choose one over the other)
Later Case study and basic math problem.
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Topics based on recent interview experiences.
Featured question at Ebay
Write a query to get the total three-day rolling average for deposits by day
| Question | |
|---|---|
| Merge Sorted Lists | |
| Experiment Validity | |
| Button AB Test | |
| Longest Streak Users | |
| Rectangle Overlap | |
| Find the First Non-Repeating Character in a String | |
| Random Bucketing | |
| P-value to a Layman | |
| Google Maps Improvement | |
| Nearest Common Ancestor | |
| Hurdles In Data Projects | |
| Target Indices | |
| Testing Price Increase | |
| Matrix Rotation | |
| New UI Effect | |
| Sample Size Bias | |
| Listing Bookings Aggregation | |
| Max Width | |
| Bias vs. Variance Tradeoff | |
| Inherited Model Evaluation | |
| String Palindromes | |
| Walking Robot | |
| Impossibly Iterative Fibonacci | |
| The Longest Journey | |
| Seller Type Modeling | |
| Azure Kubernetes Infrastructure | |
| Relational Migration | |
| Free Shipping Mention Test | |
| Processing Large CSV |
Synthesized from candidate reports. Individual experiences may vary.
One candidate reported an online CodeSignal round covering Python and SQL. Their examples included a basic Python groupby task and a SQL gaps-and-islands problem about finding streaks. Practice working through the logic aloud, including how you group records and verify the result.
Published reports describe technical discussions across Python, SQL, and machine learning. Specific examples include pandas groupby and aggregation work, plus a SQL window-function question discussed in a production-style scenario. Prepare for both syntax-level questions and follow-up questions about your reasoning.
One candidate's three-round process included project discussion, SQL, a case study, and a deeper review of projects with basic math and GenAI. Be ready to explain the methods on your resume, including why a clustering approach such as DBSCAN was selected instead of k-means and what tradeoffs shaped the choice.
