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Two Lyft Data Scientists share their intern-to-full-time journeys, highlighting impactful data science projects in EV adoption and driver loyalty.
•Morteza applied a difference-in-differences (DiD) causal model to measure the effect of EV conversion on Driver-Hour (DH), Lyft's driver productivity metric
•Key challenges included incomplete visibility into app-switcher behavior and limited home charging access, addressed via third-party data integration and public charging projections
•Han worked on the Driver Loyalty team analyzing a tiered referral bonus program, studying how Platinum drivers' referrals differ in retention and productivity
•Both emphasize Lyft's culture of practical impact: prioritizing the 20% of efforts that drive 80% of results over exhaustive academic-style analysis
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Morteza became Lyft's first EV Data Scientist after returning full-time, and his intern work was later published in a peer-reviewed journal
This summary was automatically generated by AI based on the original article and may not be fully accurate.
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