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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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