Staff Scientist, Direct

5 days ago


Bengaluru, India Uber Full time

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.



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