Lime is the largest global shared micromobility business, operating in close to 30 countries across five continents. We’re on a mission to build a future where transportation is shared, affordable and carbon-free. Our electric bikes and scooters have powered more than one billion rides in cities around the world. Named a 2025 Time 100 Most Influential Company, Lime continues to set the pace for shared micromobility globally, spurring a new generation of clean alternatives to car ownership. We are looking for a marketing analytics or applied science leader who can utilize best-in-class experimentation techniques to optimize the performance of Lime’s lifecycle marketing campaigns, pricing, promotions, and other rider incentives for a large and rapidly growing global business. This role sits at the intersection of analytics, strategy, and execution, and it involves applying machine learning to improve the ROI of campaigns of both broad reach and very precisely targeted campaigns. The ideal candidate will have a strong applied science background and be comfortable not only with basic lifetime value and next best action concepts, but also advanced multi-arm bandit, contextual bandit, and related reinforcement learning techniques. You will be expected to utilize, enhance, and tune campaign performance and targeting utilizing tools such as StatSig or Braze, as well as foundational models built internally by Lime’s data science team. You will also lead a small team and be expected to be both a hands-on analytical expert and a strategic partner—comfortable building models, guiding teams, and influencing senior stakeholders. High proficiency with big data, SQL, Python, and Tableau are also important. While you won’t be coding foundational models or publishing Tableau reports yourself, you will be directly managing data analysts and working closely with Lime’s central data science team who will. This is a remote position with a requirement for candidates residing in Canada to maintain effective collaboration across teams.
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Job Type
Full-time
Career Level
Mid Level
Number of Employees
501-1,000 employees