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.
The process consisted of 6 rounds total: 1 initial Technical Screen followed by a 5-round virtual onsite split across two days.
What surprised me the most were the Project Presentation and the Debugging round. The presentation required defending architectural tradeoffs at a very granular level. The debugging interview assessed practical engineering reflexes rather than textbook algorithms—navigating an existing codebase, identifying bugs, and improving code quality quickly, much like conducting an intensive, real-world PR review under time pressure.
I felt most confident during the Practical ML and project presentation rounds, where I could showcase domain depth and past impact. I started sweating during the debugging round due to the pacing—parsing unfamiliar repository structure and fixing edge cases against the clock tested pure speed and composure.
Questions asked: Experience round with the hiring manager: • Multi-project handling: how you juggle concurrent projects and what challenges come up. He followed with "for those challenges, how did you solve that?" • Balancing a high-stakes project against competing timelines and dependencies. • LLM-as-judge: how you designed the judge, set thresholds, and handled ambiguous or critical cases. • Stakeholder handling when requirements were vague or a team had no clear point of contact.
Coding round via CodeSignal: • Refactor a missing value imputation feature. • Then find and fix code quality issues in the codebase. • Heavier on reading and improving existing code than fresh algorithm problems.
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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.
