Staff Scientist, Tech

About The Team
Ever wondered how Uber makes it seamless to hail an auto or bike taxi in the middle of rush hour, or how pricing works for your next metered cab ride? That's the kind of real-world complexity the Hailables Science team thrives on solving.

As part of Uber's Core Analytics & Science (CAS) organization, the Hailables team focuses on our fastest-growing Mobility offerings, which include auto rickshaws, bike taxis, and metered cabs. These products operate in high-density, price-sensitive markets with unique customer behaviors and regulatory landscapes. We offer a rare opportunity to work at the frontier of data science in this space.

We partner closely with product, engineering, and operations teams to design smarter systems. It ranges from more intelligent pricing systems, efficient matching algorithms, and building personalized experience for millions of riders and drivers across emerging markets. From improving pickup reliability to shaping how supply and demand interact in highly dynamic cities, the Hailables team is where science meets street-level scale.

About The Role
As the Science Tech Lead (Individual Contributor) for the Hailables charter, you'll be at the heart of product development, guiding the science direction for a team of 5-7 talented scientists based in India. You'll work closely with Engineering and Product partners to define strategy, shape roadmaps, and drive execution. You will bring data, algorithms, and business context together to solve complex, real-world problems.

In this role, you won't just influence - you'll build. Whether it's designing machine learning models to improve matching and pricing, uncovering insights through rigorous experimentation, or applying statistical thinking to shape product levers, you'll lead by example. You'll also mentor team members, foster a culture of scientific excellence, and build strong cross-functional relationships with global stakeholders across time zones.

This is a rare opportunity to lead from the front: as a senior IC, you'll own the scientific vision while staying hands-on, solving meaningful problems that impact millions of users across Uber's fastest-growing mobility products.

Basic Qualifications

• Undergraduate and/or graduate degree in Statistics, Machine Learning, Operations Research, or other quantitative fields.
• 9+ years of industry experience as an (Applied / Data) Scientist or equivalent.
• Experience with exploratory data analysis, statistical analysis and testing, causal analysis and ML model development.
• Experience in experimental design and analysis.
• Proficiency using Python, Spark at scale with large data sets.
• Experience with tools like SQL, R in a production environment.

*Preferred Qualifications*

• Master's/Ph.D. Degree in Statistics, Machine Learning, Operations Research, or other quantitative fields.
• Experience in tech or marketplace industries.
• Experienced in partnering with cross-functional stakeholders to execute decisions.
• Ability to mentor, coach and develop junior team members.
• Superb communication and organisation skills.

What the candidate will do

• Define the science roadmap in collaboration with Product and Engineering, identifying key opportunities to improve rider and driver experience across autos, bike taxis, and metered cabs.
• Design and prototype machine learning and statistical solutions to solve core marketplace challenges like pricing, matching, and user funnel optimization.
• Lead experimentation efforts (A/B, switchback, quasi-experiments), ensuring methodological rigor and high-quality insights.
• Define and monitor key tech OKRs, ensuring the right metrics are in place and tracked accurately to guide product and engineering execution.
• Improve data quality, observability, and reliability by partnering with Engineering on logging, metrics, and pipelines.
• Conduct deep-dive analyses to generate actionable insights and influence product direction.
• Mentor scientists and promote a culture of excellence in modeling, experimentation, and analytics.
• Communicate scientific work and outcomes clearly to cross-functional stakeholders and leadership.