Lessons from Scaling Analytics at Lyft
summary
Joined Lyft as the first analytics hire and grows analytics into a 200+ org that drives decisions across a 5,000-person company while scaling rides 100x. Explains focusing on wait-time thresholds (three minutes inflection) and building city-level supply/demand models that prevent shutdown, reallocating marketing after a holdout shows 90% cannibalistic coupon spend, hiring strategy that scaled team from 0 to 60, and self-serve stack where Mode is used by ~1,000 people.
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chapters (11)
- 0:00Introduction
- 1:10Scaling Lyft
- 2:02Making do with what you have
- 5:47Solving the right problems
- 10:32Testing and repeating
- 13:16Teambuilding
- 17:31Building a Machine
- 21:03Building a Team
- 23:06Scaling DataDriven Culture
- 28:08Accountability
- 32:31Conclusion
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