Lyft
Mid
Lead Lyft's Toronto rider-experience data science team and set the experimentation bar
This interview tests people-management chops (2+ years leading DS teams) fused with hands-on rigor in experimentation and causal inference for a consumer marketplace. Expect questions on setting measurement standards for rider-facing product squads, mentoring a diverse team, and driving the team's adoption of AI-native workflows. Strong answers balance technical depth with clear, senior-leader-facing storytelling.
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What this interview tests
- People management: building, leading, mentoring a data science team
- Experimentation and causal inference rigor (A/B, multivariate, incremental lift) for a consumer product
- Translating ambiguous business questions into decision-ready, roadmap-shaping analysis
- Operationalizing ML models (propensity, segmentation, churn, personalization) with engineering
- Driving adoption of AI-native data science workflows on the team
Common question themes
Tell me about building or growing a data science team — how do you match projects to individual skills and career goals?
Describe a time you upgraded a team's experimentation or causal-inference methodology and got stakeholder buy-in
Walk through translating an ambiguous rider-experience question into an analysis that changed the roadmap
Tell me about operationalizing an ML model (e.g., churn or personalization) in partnership with engineering
How would you lead your team's transition to AI-native data science workflows without losing analytical rigor?
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