Pricing Science Lead — ML, Causal Inference & Optimization

Uber; New York (NY); On-site; USD 216,000 - 240,000; Full time

Benefits offered by this job

Bonus program

Equity

401(k) plan

Benefits

Job summary

Uber is seeking a lead data scientist to drive end-to-end product development from a data, analytics, modeling, and experimentation perspective. You will mentor a team of scientists, partner with stakeholders to translate business needs into technical roadmaps, and communicate insights to senior leadership.

Responsibilities include solving complex optimization and causal inference problems, designing experiments, and owning the end-to-end product cycle with defined metrics in partnership with

Qualifications

• Ph.D., MS or Bachelor's in Economics, Statistics, ML, OR or other quantitative fields.
• 7+ years industry experience as an Applied or Data Scientist (or 4+ years with PhD).
• 1+ years managing a team of scientists.
• Excellent communication and collaboration skills across multiple product areas.
• Background in at least one programming language (Python, R, Scala/Spark).
• Experience designing experiments and interpreting results across KPIs.

Responsibilities

• Lead a team of Scientists and mentor team members.
• Partner with cross-functional stakeholders to translate business needs into technical roadmaps.
• Communicate complex findings to senior leadership.
• Solve problems using data-driven approaches including Causal Inference, Optimization, and ML.
• Own the product development cycle end-to-end from data and modeling aspects.
• Define success metrics with cross-functional partners.

Skills

Leadership

Programming

Experiment design

Communication

Education

Ph.D./MS/BS in quantitative fields

Tools

Python

R

Scala/Spark

Job description

Uber is seeking a lead data scientist to drive end-to-end product development from a data, analytics, modeling, and experimentation perspective. You will mentor a team of scientists, partner with stakeholders to translate business needs into technical roadmaps, and communicate insights to senior leadership.

Responsibilities include solving complex optimization and causal inference problems, designing experiments, and owning the end-to-end product cycle with defined metrics in partnership with