About The RoleThe Global Strategy & Planning team at Uber Eats shapes the long-term direction of our marketplace and ensures teams across the world execute against a unified strategy. We identify growth opportunities, define where Uber Eats should play, and build the insights that guide decisions across product, commercial, and market teams.Within this team, the Competitive Merchant Intelligence pod provides an objective view of Uber Eats' restaurant and merchant selection by benchmarking it against competitors and assessing broader market potential. We quantify where Uber Eats has opportunities to expand into new areas, strengthen its presence, and deepen selection density across markets.As a Data Scientist, you will turn diverse data sources into forward-looking insights, models, metrics, and tools that illuminate Uber Eats' true opportunity landscape and guide strategic choices across the business.What You'll Do Build analytical models and frameworks that quantify market potential, merchant value, and competitive positioning to guide strategic decisions. Develop and maintain competitive metrics that benchmark Uber Eats' merchant selection against the market and highlight opportunities for expansion or densification. Design, improve, and own data pipelines and tools that ensure merchant intelligence is accurate, reliable, and scalable across global teams. Partner with Engineering, Product, and Commercial Operations to translate insights into action, shape data requirements, and drive product or operational improvements. Investigate new data sources and analytical methods to enhance Uber Eats' understanding of the merchant landscape and unlock new opportunity areas. What you'll need 1+ year of experience in a data science, analytics, or other quantitative role. Strong proficiency in Python and SQL, with experience working across large or complex datasets. Ability to translate open-ended business questions into structured analyses and clear, actionable insights. Preferred Qualifications Experience building or maintaining data pipelines, with familiarity in data engineering concepts and high-reliability data workflows. Strong attention to detail and a rigorous approach to ensuring accuracy in high-visibility metrics and analyses. Experience working with external, web-scraped, or unstructured data and transforming it into reliable analytical products. Background in market sizing, forecasting, or opportunity evaluation frameworks. Experience developing dashboards or data visualizations that communicate insights to non-technical stakeholders.