Data Scientist, People Analytics

At Uber, People Analytics drives business performance by translating employee insights into strategic action. We partner with senior leaders to improve organizational effectiveness, employee engagement, and long-term workforce outcomes across the full talent lifecycle-from hiring to retention.

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

• Lead high-impact analytics that inform strategic decisions on engagement, retention, performance, and organizational health
• Translate ambiguous business questions into structured analytical frameworks and measurable outcomes

Apply advanced quantitative methods to people data

• Use statistical modeling, causal inference, experimentation, and/or machine learning to understand employee behavior and outcomes
• Work with messy, real-world people data and integrate multiple data sources (survey, HRIS, behavioral data)

Evolve employee listening analytics

• Analyze Uber-wide survey data and behavioral signals (e.g., organizational network analytics)
• Improve measurement approaches (survey design) and uncover actionable insights on employee experience

Influence stakeholders at all levels

• Communicate complex findings clearly to both technical and non-technical audiencesAct as a thought partner to senior leaders, shaping decisions with data-backed recommendations

• Master's or PhD in a quantitative field (e.g., I/O Psychology, Organizational Behavior, Behavioral Economics, Statistics, Data Science, or related)
• Proficiency in SQL2+ years of industry experience using Python or R to analyze large-scale datasets, * PhD with 1+ years, or MS with 3+ years of experience in applied research or data science (ideally in a fast-paced, tech environment)
• Strong foundation in statistical modeling, causal inference, experimentation, and/or machine learning
• Background in people analytics, HR data, or behavioral science applications
• Experience with survey design and measurement, and/or organizational network analysis (ONA)Demonstrated ability to leverage GenAI tools to automate, augment, or scale data analysis, insight generation, or research workflows