Staff Scientist, Direct & Connect

Job Description

About The Role We're hiring a Staff Scientist for Uber's Direct, Connect, and Grocery Delivery team-three of the company's fastest-growing business lines redefining the future of on-demand logistics. From retail returns and prescription deliveries to nationwide shipments and grocery fulfillment, this team empowers customers to get anything delivered, anytime. In this role, you'll lead the scientific strategy to create a best-in-class user experience across Uber's delivery platforms. You'll drive both merchant and consumer growth through funnel analysis, user targeting, promotion optimization, and SKU-level insights-ensuring every decision is grounded in rigorous, data-driven thinking. We're looking for someone with deep analytical expertise and a strong business mindset -an individual who can translate complex data into clear, actionable insights and help shape the strategic direction of our products. What You'll Do Serve as Science Tech Lead (IC) for a team based in India, supporting Uber's Direct, Connect, and Grocery Delivery business. Apply advanced analytics, including statistical modeling, and causal inference, to improve delivery experiences. Translate complex data into actionable insights for executives and cross-functional stakeholders. Collaborate with Product, Engineering, and Ops to drive a data-informed product roadmap. Mentor scientists and promote best practices in data science and machine learning. Foster a culture of scientific rigor and strong cross-functional collaboration. What You'll Need Undergraduate and/or graduate degree in Statistics, Machine Learning, Operations Research, or other quantitative fields. 9+ years of industry experience as an (Applied / Data) Scientist or equivalent. Experience with exploratory data analysis, statistical analysis and testing, causal analysis and ML model development. Experience in experimental design and analysis. Proficiency using Python, Spark at scale with large data sets. Experience with tools like SQL, R in a production environment. Show more Show less