Staff Data Scientist, Global Analytics Leader

Uber; Sunnyvale (CA); Hybrid; USD 198,000 - 242,000; Full time

Benefits offered by this job

Bonus program

Benefits

Job summary

Uber is seeking a Staff Data Scientist to serve as a technical North Star for our core platforms and global marketplaces, turning complexity into strategic clarity and guiding decisions a year out. You will mentor others, partner with senior product, engineering, and operations leaders, and help build scalable data frameworks that drive movement at scale.

This role emphasizes high-leverage influence and productionizing ML models.

Qualifications

• Bachelor's degree in Engineering, IT, Information Systems, Math/Stats or related field with five years of post-baccalaureate experience.

Responsibilities

• Leverage data to perform intensive analysis across business areas to drive product development.
• Design experiments and analyze results to draw conclusions.
• Perform time-series analyses, hypothesis testing, and causal analyses to assess impact and trends.
• Build models to understand user behavior and predict future performance.
• Create dashboards and reports to communicate results and monitor key metrics.
• Extract data from data warehouses, back-test models, and compare performance.
• Develop end-to-end solutions addressing funnel optimization, engagement, and user experience.
• May telecommute.

Skills

Python

R

SQL

A/B testing

Time-series analysis

Statistical analysis

Machine learning

Experiment design

Education

Bachelor's degree in Engineering / IT / Information Systems / Mathematics / Statistics or related field

Tools

SQL Databases

Job description

Uber is seeking a Staff Data Scientist to serve as a technical North Star for our core platforms and global marketplaces, turning complexity into strategic clarity and guiding decisions a year out. You will mentor others, partner with senior product, engineering, and operations leaders, and help build scalable data frameworks that drive movement at scale.

This role emphasizes high-leverage influence and productionizing ML models.