Manager II, Science - Earner (Economics)

Manager II, Science - Earner (Economics) Department: Data Science Team: Data Scientist Location: New York, United States Type: Full-Time **About the Role** Earners (drivers and couriers) are an integral part of Uber’s multi-sided marketplace. They provide the time and the means to move people and things. Importantly, they enable the connection between the physical and digital world to make the movement happen at the push of a button for everyone, everywhere. Within Uber, the Earner team plays a critical role in earners’ journey as the team is responsible for earner onboarding, activation, early life cycle, and resurrection. This presents the teams with the opportunity to shape and tailor the product experience during earners’ many firsts (i.e., first time interacting on the Uber platform, choosing the earning opportunity, going online, receiving incentive offers, completing a trip, or reading the earnings summary). These firsts can be daunting. Therefore, making sure that the earner journey is great at every touch point is important to build trust with Earners, communicate Uber’s value proposition, and ensure each firsts to be a great experience. As a Science Manager, you will play a pivotal role in shaping the future of Earners at Uber. Partnering closely with Engineering and Product leadership, you will drive the strategic vision and execution of Earner related product development with a team of Scientists who work on complex product and lifecycle analysis, experimentation, measurement foundations, and modeling. **What You'll Do** - Lead a global team of highly skilled scientists, providing mentorship, feedback, and career growth opportunities to foster technical excellence and impact. - Refine ambiguous questions and generate new hypotheses about the product through a deep understanding of the data, our Earners, and our business. - Develop and execute robust experimental designs and insightful analyses to shape the product strategy, proactively identifying and mitigating potential challenges. - Develop and implement cutting-edge statistical, analytical, and ML solutions to enhance and person