Airbnb's Data Scientist interviews reportedly start with an unusually rigorous, behavior-focused recruiter screen and can extend into a SQL exam, an A/B testing case study, and a two-part presentation, based on two candidate reports.
$188K
Avg. Base Comp
$336K
Avg. Total Comp
Not reported
Typical Rounds
Not reported
Process Length
Airbnb's Data Scientist interview reports are limited to two accounts, but together they describe a process built around behavioral depth and experimental design rigor rather than pure coding speed. One candidate who applied as a remote Data Scientist found that even the initial recruiter screen felt unusually thorough, with detailed questions about past projects and motivation for the role that resembled a hiring-manager conversation more than a routine alignment call — a sign that culture and motivation may be assessed from the very first touchpoint. A separate candidate, interviewing for a closely related analytics leadership track, experienced a longer path: an initial recruiter call that was cancelled shortly before it was due, a brief HR screen, a hiring-manager conversation about experimentation background, and then a technical stretch covering a 45-minute SQL exam, a 45-minute experimentation case study centered on controlling for seasonality in A/B tests, and a two-part presentation covering about 20 minutes of analysis plus a past project.
Since this later-stage detail comes from a single report on a different but related role, treat it as one documented path rather than a guaranteed script for every Data Scientist loop. Even so, the clearest preparation takeaway is to have concrete, defensible experimentation examples ready — the case study pushed past surface-level A/B testing knowledge into how you'd control for confounds, and the presentation format rewards explaining your reasoning clearly, not just producing a correct SQL query.
Synthesized from 2 candidate reports by our editorial team.
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Real interview reports from people who went through the Airbnb process.
I had an initial screen with Airbnb as part of my passive job search for remote roles. What stood out was that the recruiter screen felt unusually rigorous — more like a hiring manager or culture screen than a typical recruiter call. The recruiter asked detailed questions about my motivation, past projects, and had a noticeably more thorough approach compared to other companies where recruiter calls are mostly alignment checks.
The conversation touched lightly on technical topics but was primarily focused on behavioral and motivational fit. I was still in early stages and hadn't yet reached a full technical round, so I can't speak to the depth of their technical assessment. Overall it was a positive experience, though the elevated rigor of even the recruiter screen was a signal that Airbnb takes culture and motivation seriously from the very first touchpoint.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Airbnb
Write a query that returns all neighborhoods that have 0 users.
| Question | |
|---|---|
| Order Addresses | |
| Uber User Journey | |
| Causal Email Journey | |
| Hurdles In Data Projects | |
| Success Measurement | |
| Trial User Segmentation | |
| Listing Bookings Aggregation | |
| Data Pipelines and Aggregation | |
| Model Product Performance Degradation | |
| String Palindromes | |
| Approval Drop | |
| Deciding Between Solutions | |
| Client Solution Pushback | |
| Payment Data Pipeline | |
| Underpricing Algorithm | |
| Reward Experiment | |
| Google Docs Drop | |
| Docs Metrics | |
| Statistically Significant Test | |
| Listings Recommendation | |
| Dynamic Demand Pricing | |
| Interpreting Fraud Detection Trends | |
| Experiment Validity | |
| 2nd Highest Salary | |
| Rolling Bank Transactions | |
| Employee Salaries | |
| Merge Sorted Lists | |
| Button AB Test | |
| Monthly Customer Report |
Synthesized from candidate reports. Individual experiences may vary.
One candidate who applied as a Data Scientist reported that Airbnb's recruiter screen felt unusually rigorous — more like a hiring-manager or culture interview than a typical alignment check — with detailed questions about past projects and motivation for the role. A separate candidate interviewing for a related analytics role described an initial recruiter call that was cancelled shortly before it was due, followed by a brief HR screen, suggesting scheduling may be inconsistent even at this early stage.
After the HR screen, one candidate moved into a hiring-manager conversation focused specifically on their experimentation background. This stage appears to gauge whether a candidate's analytical experience, particularly around A/B testing and causal reasoning, fits the team's day-to-day work before advancing into the more technical rounds that followed.
One candidate's technical round consisted of a 45-minute SQL exam paired with a separate 45-minute experimentation case study. The case study required explaining how to control for confounds such as seasonality in an A/B test, indicating that Airbnb's data science interviews may probe experimental design depth rather than just query-writing speed, and candidates should be ready to reason through statistical tradeoffs out loud.
The same candidate then completed a presentation round: about 20 minutes walking through an analysis and roughly 25 minutes presenting a past project of their own. This format suggests the team wants to see both analytical reasoning and a candidate's ability to clearly communicate prior work, not just correct final answers.
