Analytics Lead, LUS

Lyft · Toronto, Canada

Experience: 3-5+ years

At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive. Data and analytics are at the heart of Lyft's products and decision-making. As a member of the Lyft Urban Solutions team, you will play a key role in shaping the future of bikeshare by leveraging data to improve the performance of our bikeshare and scooter markets across America. A successful candidate thrives in a dynamic and collaborative environment, has a natural curiosity, and isn’t afraid to dive deep. In this role, you will collaborate closely with Operations, Policy, Engineering, Product and Finance teams to drive data-informed strategies that align our operations and products with city transportation goals and user needs. You will work in a fast-paced environment where analytical insights directly impact decisions ranging from fleet management and operations performance to pricing and product features to long-term investments in bikesharing infrastructure. We’re looking for a passionate and driven Data Analyst to tackle some of the most complex and impactful challenges in micromobility. If you’re excited about shaping the future of urban mobility through data, we’d love to hear from you. Responsibilities: Partner with Product, Engineering, Policy, Operations, Finance and other cross-functional stakeholders on initiatives to conduct deep-dive analyses to root cause issues and propose solutions Develop frameworks, business logic and scalable processes to streamline reporting and drive decision-making Forecast operational requirements needed to maintain high service levels and meet contractual and financial targets Work closely with cross-functional partners to deliver data quickly, reliably and accurately to our city partners Monitor and diagnose KPI performance and present findings to senior leadership Experience: 3-5+ years experience in data analytics in a high-growth environment, preferabl

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