
Airbnb
Staff
Own the causal inference frameworks that turn Airbnb's short-term experiment results into trustworthy long-term impact estimates.
Airbnb's Core Data Science team is hiring a Staff Data Scientist to build and own frameworks that estimate the long-term impact of product changes, not just their short-term experiment reads. You'll develop causal inference methodology (experimental, econometric, quasi-experimental) that other DS teams across the company can reuse. This interview probes causal inference depth, framework-building judgment, and the ability to translate long-term impact estimation from theory into something that changes real product decisions.
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What this interview tests
- Causal inference methodology (experimental, econometric, quasi-experimental)
- Connecting short-term metric movement to long-term impact estimates
- Building reusable frameworks/tooling for other DS teams
- Heterogeneous treatment effect analysis
- Stakeholder communication across technical and non-technical audiences
Common question themes
Walk through a time you estimated the long-term impact of a change using only short-term data
How would you validate that a causal inference framework generalizes across metrics/teams
Trade-offs between experimental, econometric regression, and quasi-experimental approaches
Designing an evaluation framework to detect heterogeneous impact across user segments
How you'd communicate uncertainty in a long-term impact estimate to a non-technical exec
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