Uber Staff Machine Learning Scientist 

United States, West Virginia 

395860184

You’ll apply your expertise inwith a specific focus onto build models that assess and manage financial exposure across our payments systems. We're looking for experienced individuals who are passionate about solving complex, real-world problems at scale using data.

• Design, develop, and deploy machine learning, statistical, and optimization models into Uber’s production systems to support a range of payments-related applications.

• Build underwriting models to evaluate financial exposure and inform risk-related decision-making within Uber’s payments ecosystem.

• Collaborate closely with cross-functional teams including Product, Engineering, Operations, and Design to take projects from concept through production.

• Analyze product and system performance to identify data-driven opportunities for improvement and innovation.

• Communicate insights and recommendations clearly to senior stakeholders , influencing product and business strategy.

• 7+ years of hands-on experience in roles such as Machine Learning Scientist, Research Scientist, or ML Engineer.

• Proven track record of building and deploying ML, statistical, or optimization models in real-time or large-scale production systems.

• Strong programming skills in Python (or a similar language), with experience working with large-scale data pipelines and systems.

• Proficient in SQL and PySpark .

• Advanced degree ( M.S. or Ph.D. ) in a quantitative field such as Computer Science, Machine Learning, Statistics, Economics, or Operations Research.

• Experience in underwriting or financial risk modeling.

• Demonstrated thought leadership in leading end-to-end, cross-functional projects from ideation through deployment.

• Experience working in payments , fintech, or financial services domains, especially in the context of credit risk or exposure modeling.

• Ability to influence technical direction and decision-making in complex systems.

* Accommodations may be available based on religious and/or medical conditions, or as required by applicable law. To request an accommodation, please reach out to .