Senior Data Scientist, Rider Team at Lime
United States, , USA -
Full Time


Start Date

Immediate

Expiry Date

05 Dec, 25

Salary

207000.0

Posted On

06 Sep, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Good communication skills

Industry

Information Technology/IT

Description

Lime is the world’s largest shared electric vehicle company. We’re on a mission to build a future where transportation is shared, affordable and carbon-free. Our electric bikes and scooters have powered 700+ million rides in 250+ cities on 5 continents, replacing an estimated 150+ million car trips. Named a Time 100 Most Influential Company and Fast Company Brand That Matters, Lime continues to set the pace for shared micromobility globally.
As a Senior Data Scientist on Lime’s Rider DSA team, you will play a critical role in helping us understand our riders and uncover the drivers of retention. You’ll focus on experimentation, product analytics, and data deep-dives that surface actionable insights for product and business teams. Partnering with Product, Engineering, and Operations, you’ll help shape strategy on initiatives like early-funnel adoption, second-month retention, and loyalty programs—turning data into measurable impact across Lime.

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Responsibilities
  • Design, run, and analyze experiments to optimize rider experience and improve retention outcomes.
  • Define project goals, success metrics, and measurement frameworks to ensure clarity and alignment across initiatives.
  • Deliver data-driven solutions by translating complex analyses into clear, actionable recommendations in compelling ways that influence strategy and roadmap decisions.
  • Partner with Product and other teams to evaluate and refine retention-focused initiatives such as onboarding flows, memberships, and loyalty programs.
  • Conduct deep-dive analyses to identify patterns, diagnose problems, and inform strategic decisions around rider engagement.
  • Identify key “aha” moments that predict long-term retention and quantify their impact on rider behavior.
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