Figma
Mid
Bring PhD-level rigor to Figma's experimentation platform and AI-feature measurement
Figma's Core Data team builds the experimentation, analytics, and AI tooling every product team relies on. This role explicitly requires a PhD in a quantitative field and asks you to advance A/B testing and causal inference methodology, build ML-based analytical frameworks, and help define how AI-powered features get measured. Good prep if you want to practice defending a causal inference or experimentation design choice the way a platform team (not just a single product team) would scrutinize it.
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What this interview tests
- Causal inference technique selection and assumption-checking
- Improving A/B testing/experimentation platform methodology (not just running experiments)
- Building reusable, robust ML-based analytical frameworks
- Measuring impact and quality of AI-powered features specifically
- Owning ambiguous data projects end-to-end
- Communicating technical/statistical concepts to non-technical audiences
Common question themes
Walk me through a causal inference method you've used and where its assumptions would break in production
Tell me about a time you improved an experimentation or A/B testing framework itself, not just ran an experiment
Describe building an analytical framework or tool meant to be reused by other data scientists
How would you design measurement for an AI-powered feature differently than a normal product change
Tell me about owning a complex, ambiguous data project end-to-end
How do you explain a statistical tradeoff to a non-technical product stakeholder
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