Expedia Data Scientist candidates report behavioral interviews, SQL/Python and experimentation work, product case discussions, and stakeholder communication. One advertising-platform report also included an ad-relevance trade-off case.
$143K
Avg. Base Comp
$171K
Avg. Total Comp
4 rounds
Typical Rounds
2-4 weeks
Process Length
For Expedia Data Scientist roles, the clearest preparation theme is connecting technical work to product and stakeholder decisions. One candidate described four rounds after the recruiter conversation: a hiring-manager discussion, a SQL/Python and A/B-testing technical round, a product-sense case with a panel, and a product-and-stakeholder conversation. Their behavioral prompts asked for an introduction, a complex problem, and an example involving a diverse viewpoint; they emphasized tying past experience to what could have been done better.
A separate candidate for an advertising-platform role encountered behavioral discussions with the hiring manager, a mixed product-manager and advertising-manager panel, and a final technical conversation. That report named SQL queries, subqueries, and window functions such as LAG, alongside Tableau, conceptual machine learning, and detailed discussion of CV projects. Practice explaining how an analysis changes a product decision, rather than treating SQL or modeling as isolated exercises.
The advertising report also included a case about choosing between a more relevant, lower-bidding hotel ad and a less relevant, higher-bidding alternative. Be ready to articulate the revenue-versus-user-relevance trade-off and the metrics you would use to judge it. The available reports are limited, so team-specific emphasis may differ.
Synthesized from 2 candidate reports by our editorial team.
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Real interview reports from people who went through the Expedia, Inc. process.
Total 4 rounds not including recruiter round -
Questions asked: Important to tie back all your experiences and what you could do better with their principles. eg: Tell me about yourself? What's the most complex problem you've worked on? Whats's the most diverse viewpoint problem statement have you worked on?
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Expedia, Inc.
How would you assess the validity of the result?
| Question | |
|---|---|
| Random SQL Sample | |
| Permutation Palindrome | |
| Completed Shipments | |
| Bagging vs Boosting | |
| Revenue Retention | |
| Average Commute Time | |
| Significance Time Series | |
| Google Maps Improvement | |
| Average Ride Duration | |
| Nearest Common Ancestor | |
| Groups of Anagrams | |
| Target Indices | |
| Xgboost vs Random Forest | |
| Average Revenue per Customer | |
| Hurdles In Data Projects | |
| Lasso vs Ridge | |
| Forecasting New Year Revenue | |
| Count Transactions | |
| Banner Ad Strategy Success | |
| Implementing the Fibonacci Sequence in Three Different Methods | |
| Bias vs. Variance Tradeoff | |
| Data Preparation for Imbalanced Data | |
| Shoe Demand Seasonality | |
| String Palindromes | |
| Best Performing Advertisers | |
| Deciding Between Solutions | |
| Check Matching Parentheses | |
| Increase Search Ads | |
| Client Solution Pushback |
Synthesized from candidate reports. Individual experiences may vary.
Candidates report a hiring-manager conversation centered on behavioral and experience-based questions. Examples included describing a failure or unexpected project outcome, a complex problem, and communicating technical findings to non-technical stakeholders.
One candidate explicitly reported a technical round covering SQL, Python, and A/B testing. Another described SQL query writing, subqueries, and window functions such as LAG; Python coding was minimal in that advertising-platform process.
Candidates report a product-sense case with a panel or a mixed product-manager and advertising-manager panel. Expect to discuss dashboards, deriving insights, and how analysis informs a product or business decision.
One candidate reported a product-and-stakeholder behavioral round. In an advertising-platform interview, a candidate was asked to weigh a higher bid against user relevance for limited ad space, so advertising-oriented teams may probe that trade-off.
