Job
Description
Uber Eats is seeking a highly skilled and motivated Staff Data Scientist to join our Search Team. In this role, you will be a key driver in enhancing the search experience for millions of users across the globe. You'll bring deep statistical expertise to guide decision-making, improve product performance, and ensure our evaluations and insights are rooted in methodological rigor. What You'll Do Conduct robust statistical analyses on complex datasets to identify product opportunities, shape roadmap priorities, and optimize user experiences in search. Design and evaluate A/B tests and quasi-experiments, applying best practices in experimental methodology to ensure high-quality, unbiased insights. Build and maintain statistical frameworks for defining and measuring ground truth - identifying reliable signals to evaluate search relevance, personalization, and user satisfaction. Apply advanced sampling strategies to construct representative datasets for both offline and online evaluation pipelines, ensuring scalability and statistical power. Develop rigorous evaluation metrics that reflect real-world product performance and align closely with user and business goals. Lead initiatives to strengthen causal inference practices across the team, applying methods like matching, regression discontinuity, and difference-in-differences where appropriate. Partner with product managers, engineers, and other scientists to translate open-ended product questions into structured analytical approaches. Provide mentorship and technical leadership to other scientists, promoting a culture of statistical excellence and continuous learning. What You'll Need: M.S. or Bachelor's degree in Statistics, Economics, Mathematics, Operations Research, Computer Science, or a related quantitative field. 10+ years of industry experience in data science or applied analytics, ideally with experience in consumer products, search, or recommendation systems. Deep expertise in ground truth design, and evaluation methodologies for complex user-facing systems. Proven experience with statistical sampling techniques and working with offline evaluation pipelines to assess models and product performance. Strong fluency in experimentation design, causal inference, and observational data analysis. Proficiency with tools like SQL, Python, and R for data manipulation, modeling, and visualization. Excellent communication skills - able to present statistical findings clearly and influence product and engineering decisions through data. A strong product sense and the ability to balance analytical rigor with practical business impact. Show more Show less
