### **About the Role** We are seeking a **Staff Machine Learning Scientist** to join Uber’s **Payments Data Science & Analytics** team. This role offers the opportunity to make a direct impact on optimizing and streamlining Uber’s global payments infrastructure. You’ll apply your expertise in **machine learning, statistics, economics, and operations research -** with a specific focus on **underwriting and financial risk modeling -** to 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. * * * ### **What You Will Do** - 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. * * * ### **Basic Qualifications** - **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. * * * ### **Preferred Qualifications** - 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. We welcome people from all backgrounds who seek the opportunity to help build a future where everyone and everything can move independently. If you have the curiosity, passion, and collaborative spirit, work with us, and let’s move the world forward, together. Offices continue to be central to collaboration and Uber’s cultural identity. Unless formally approved to work fully remotely, Uber expects employees to spend at least half of their work time in their assigned office. For certain roles, such as those based at green-light hubs, employees are expected to be in-office for 100% of their time. Please speak with your recruiter to better understand in-office expectations for this role. \*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 [accommodations@uber.com](mailto:accommodations@uber.com).