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Lyft is seeking an Analytics Lead for the Airports team to perform rigorous data deep dives, identify growth opportunities, and measure the impact of product changes. You will partner with engineers, PMs, and designers to translate insights into impactful actions across pricing, earnings, and supply at airports.
The role requires 4-7+ years in strategic data analytics, strong SQL skills, and the ability to lead high-visibility projects while communicating effectively to diverse audiences.
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 is at the heart of Lyft's products and decision-making. You will leverage data and rigorous, analytical thinking to shape our products and make business decisions. This will involve performing rigorous product deep dives, identifying opportunities for product enhancements, and measuring the impact of product changes.
The Airports team, within the Driver organization, focuses on the airport marketplace and the unique products designed for this use case. Airports are one of the most important, impactful, and complex parts of Lyft's Rideshare business, as they are a key part of both the rider and driver Lyft experience and have unique dynamics. As an Analytics Lead on the Airport team, you will collaborate with our team of engineers, product managers, and designers to conduct thorough data deep dives on airport performance, challenge our current strategy, and recommend enhancements to facilitate market growth.
The ideal candidate can apply strong business acumen to propose product and marketplace changes, and is comfortable working with a highly cross functional team. In this role, you will help us tackle problems such as:
How should we determine pricing and earnings for airport rides?
Who are our current airport drivers and what segment can we focus on to grow our supply? How does this strategy interact with driver bonuses?
Which airports are underperforming and which key metrics can we use to identify and classify airport performance? What targets should we set for these key metrics and how do we efficiently monitor these metrics?
What is driving high cancellation rates today and which types of riders/drivers are cancelling? What product changes can we implement to reduce cancellation rates?
How are riders and drivers using different ride modes at airports, and how do we design products to address this behavior?