Lyft
Senior
Lead Lyft's ad algorithms team — targeting, bidding, and revenue science
Lyft is hiring a Data Science Manager to lead the Applied Science, Data Science, and ML Engineering team behind Lyft Media (Lyft's ads business) — the algorithmic layer for ad targeting, yield, and measurement across in-app, in-car, and bikeshare inventory. This interview probes both people-management chops (8+ years technical, 3+ years leading multi-disciplinary teams) and hands-on depth in causal inference and production ML for computational advertising.
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What this interview tests
- Leading multi-disciplinary DS/Applied Science/MLE teams
- Causal inference and experimentation design for ads
- Ad targeting, bid optimization, attribution, yield management
- Bridging research to production ML systems
- Cross-functional influence with Product/Eng/Sales
- Setting technical vision tied to revenue goals
Common question themes
Tell me about a time you set technical vision for a team and it changed the roadmap
How would you design an experiment to measure the causal impact of an ad-ranking change on advertiser ROI and rider experience
Describe mentoring a struggling or underperforming scientist on your team
How do you decide what to build vs. buy for a bid optimization system
Walk me through how you'd diagnose an attribution model that's overcrediting a channel
How have you balanced platform revenue against user/rider experience in a marketplace