Expedia Data Scientist interview typically runs 4 rounds: recruiter screening, hiring manager, panel, final technical. It usually takes a few weeks and is notably behavioral-heavy before the technical round.
$119K
Avg. Base Comp
$128K
Avg. Total Comp
4
Typical Rounds
2-4 weeks
Process Length
Our candidates report that Expedia’s Data Scientist interviews, especially on the advertising side, reward people who can connect analytics to business trade-offs. The standout case about choosing between a more relevant hotel and a higher-bidding hotel shows that they care about relevance versus revenue, not just whether you can name the right metric. We’ve also seen questions around banner ad strategy, best-performing advertisers, and increasing search ads, which suggests the team wants candidates who can reason in marketplace terms and explain why one decision helps the product more than another.
A recurring theme is that Expedia leans heavily on practical communication and product judgment. Multiple candidates described behavioral questions about failure, recovery, and how to present technical findings to non-technical stakeholders, and those answers seemed to matter as much as the technical ones. In the more technical conversations, SQL depth came through clearly — subqueries, window functions like LAG, and careful query construction — while Python coding was minimal. That pattern tells us they are screening for analysts who can work independently with data, not just people who can solve abstract coding puzzles.
We’ve also noticed that the machine learning discussion stays conceptual rather than overly theoretical, with topics like bagging vs. boosting, XGBoost vs. Random Forest, and bias-variance tradeoffs appearing alongside CV project deep-dives. The non-obvious differentiator here is domain fluency in ads and experimentation: candidates who can talk through auction logic, dashboard interpretation, and experiment validity tend to sound much more credible. In short, Expedia seems to value someone who can move comfortably between SQL, product thinking, and ad-tech intuition without losing the business context.
Synthetized from 1 candidates reports by our editorial team.
Had an interview recently?
Share your experience. Unlock the full guide.
Real interview reports from people who went through the Expedia, Inc. process.
I interviewed for a Data Scientist role on Expedia's advertising platform — they have a broader product suite beyond just flights and travel booking. The process started with a recruiter screening where she walked me through the role expectations and asked about my projects. From there, I moved to a hiring manager round that was entirely behavioral — questions like "share a failure and how you came back from it" and "how do you communicate technical findings to non-technical stakeholders." Pretty standard stuff, but it set the tone for the rest of the process.
The next round was a panel with both a product manager and an advertising manager. It was a mix of behavioral and light technical questions, but behavioral dominated. The PM focused on scenarios around data dashboards and deriving insights, while the advertising manager asked domain-specific questions about the advertising space. The final round was the most technical — conducted by the team lead and a senior colleague. They went deep on SQL (writing queries, subqueries, window functions like LAG), touched on Tableau, and covered machine learning conceptually. Python coding was minimal. They also discussed my CV projects in detail.
One question that really stood out was an advertising-specific case study: given limited ad space on a page, you have two hotels — Hotel A is more relevant to the user but bids less, while Hotel B bids more but is less relevant. What do you do? It was essentially asking me to reason through the trade-offs of a bidding algorithm, balancing revenue against relevance. That kind of domain-specific, scenario-based question wasn't something I had seen in standard prep materials, so it caught me off guard. My advice: if you're interviewing for an advertising-adjacent data science role, make sure you understand the fundamentals of ad auctions and relevance scoring.
Share your own interview experience to unlock all reports, or subscribe for full access.
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 | |
| Completed Shipments | |
| Permutation Palindrome | |
| Bagging vs Boosting | |
| Average Commute Time | |
| Revenue Retention | |
| Average Ride Duration | |
| Significance Time Series | |
| Google Maps Improvement | |
| Nearest Common Ancestor | |
| Groups of Anagrams | |
| Hurdles In Data Projects | |
| Target Indices | |
| Average Revenue per Customer | |
| 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 | |
| String Palindromes | |
| Best Performing Advertisers | |
| Check Matching Parentheses | |
| Deciding Between Solutions | |
| Increase Search Ads | |
| Client Solution Pushback | |
| Boosting Instagram Stories | |
| Xgboost vs Random Forest | |
| Your Strengths and Weaknesses |
Synthesized from candidate reports. Individual experiences may vary.
The process begins with a recruiter screening call where the recruiter walks through the role expectations and asks about your background and projects. This stage is used to confirm fit for the Data Scientist role on Expedia's advertising platform.
Next is a hiring manager round that is entirely behavioral. Expect questions about past failures, how you recovered from them, and how you communicate technical findings to non-technical stakeholders.
This round is a panel with a product manager and an advertising manager. It combines behavioral and light technical questions, with the PM focusing on dashboard scenarios and deriving insights, and the advertising manager asking domain-specific questions about the advertising space.
The final round is the most technical and is conducted by the team lead and a senior colleague. It covers SQL in depth, including queries, subqueries, and window functions like LAG, along with Tableau, conceptual machine learning, and detailed discussion of your CV projects. A domain-specific ad auction case study may also be included, such as balancing relevance versus bid value for limited ad inventory.