Data Scientist, People Analytics

Data Scientist, People Analytics Department: Data Science Team: Data Scientist Location: San Francisco, United States Type: Full-Time **About the Role** We are looking for a data scientist who combines strong quantitative rigor with a deep interest in human behavior. This role sits at the intersection of business analytics consulting and employee listening, shaping company-wide decisions through advanced analytics, survey insights, and behavioral data. **Role and Responsibilities** **Drive company-level workforce strategy** 1. Lead high-impact analytics that inform strategic decisions on engagement, retention, performance, and organizational health 2. Translate ambiguous business questions into structured analytical frameworks and measurable outcomes **Apply advanced quantitative methods to people data** 1. Use statistical modeling, causal inference, experimentation, and/or machine learning to understand employee behavior and outcomes 2. Work with messy, real-world people data and integrate multiple data sources (survey, HRIS, behavioral data) **Evolve employee listening analytics** 1. Analyze Uber-wide survey data and behavioral signals (e.g., organizational network analytics) 2. Improve measurement approaches (survey design) and uncover actionable insights on employee experience **Influence stakeholders at all levels** 1. Communicate complex findings clearly to both technical and non-technical audiences 2. Act as a thought partner to senior leaders, shaping decisions with data-backed recommendations **What the Candidate Will Need / Bonus Points** **\-\-\-\- Basic Qualifications ----** 1. Master’s or PhD in a quantitative field (e.g., I/O Psychology, Organizational Behavior, Behavioral Economics, Statistics, Data Science, or related) 2. Proficiency in SQL 3. 2+ years of industry experience using Python or R to analyze large-scale datasets **\-\-\-\- Preferred Qualifications ----** 1. PhD with 1+ years, or MS with 3+ years of experience in applied research or data science (ideally in a fast-paced, tech environment) 2. Strong foundation in statistical modeling, causal inference, expe