Staff Scientist, Airports and Travel

Uber Uber · Consumer · San Francisco, CA · Data Science

Staff Scientist role focused on identifying and solving complex business problems within the Airports and Travel segment at Uber. The role involves strategic problem finding, leading end-to-end data-driven solutions, advanced experimentation, defining business metrics, and influencing senior leadership. While the role mentions using AI/Agentic tools as a preferred qualification, its core function is not AI/ML development but rather applying data science and experimentation to business challenges.

What you'd actually do

  1. Proactively identify, frame, and quantify the most ambiguous, high-leverage growth opportunities for the business, translating them into actionable, organization-level projects.
  2. Identify business opportunities and transform them into rigorous, data-driven solutions to build the next generation of end-to-end experiences for Uber travelers by partnering with engineering, product, and operations to co-drive execution.
  3. Design, own, and rigorously analyze complex experimentation frameworks, including those dealing with multi-market attribution, to provide definitive launch recommendations and set the scientific agenda for the team.
  4. Define and champion the adoption of canonical business health metrics across the organization, partnering with data engineering to build scalable, reliable data and measurement infrastructure.
  5. Synthesize complex data into clear, persuasive narratives and communicate strategic recommendations to senior leadership (Director/VP level), mobilizing cross-functional alignment across product, engineering, and operations.

Skills

Required

  • Python
  • Pandas
  • SQL
  • A/B testing
  • Causal inference
  • Statistical rigor
  • Communication skills
  • Data analysis
  • Modeling

Nice to have

  • PhD in Economics, Operations Research, or a related quantitative field
  • Leading multi-quarter analytical workstreams
  • Two-sided marketplace dynamics
  • Network effects
  • Cross-market spillovers
  • User behavior related experiments
  • AI/Agentic tools
  • High-growth, high-scale technology or marketplace domain

What the JD emphasized

  • Expert proficiency with Python, Pandas and SQL for large-scale data analysis, modeling, and production-level code collaboration.
  • Expertise in advanced experimentation (A/B testing, causal inference) and applying statistical rigor to complex business problems.
  • Experienced in using AI/Agentic tools to accelerate data retrieval, analysis, and experimentation workflows, achieving a 10x improvement in speed and impact.