Machine Learning Scientist II, Payments

About The Role

We are looking for a Machine Learning Scientist to join our Payments Data Science and Analytics team and make meaningful contributions to our mission to streamline and optimize Uber's global payment experiences. In this role, you will be able to use your strong quantitative skills in the fields of machine learning, statistics, economics, operations research to improve the Uber Payments experience. Example projects we work on include implementing machine learning models to predict which payment method a user is most likely to use and prioritizing those options in the checkout flow.

What You Will Do

• Design, build, deploy machine learning, statistical, optimization models into Uber production systems for a wide range of applications.
• Collaborate with multi-functional teams across areas such as product, engineering, operations, and design to drive system development end-to-end from conceptualisation to productionization.
• Understanding product performance and to find opportunities within data.
• Present findings to senior management to influence business decisions.
  
  

Basic Qualifications

• 3+ years of proven experience as a Machine Learning Scientist, Machine Learning Engineer, Research Scientist or equivalent.
• Experience in production coding and deploying ML, statistical, optimization models in real-time systems.
• Ability to use Python or other programming languages to work efficiently at scale with large data sets in production systems.
• Proficiency in SQL, PySpark.
• Thought leadership to drive multi-functional projects from conceptualisation to productionization.
• M.S. or Ph.D. in Computer Science, Machine Learning, Statistics, Economics, Operations Research, or other quantitative fields.
• Experience working in payments, fintech, or financial services domains.
  
  

Preferred Qualifications

• Demonstrated thought leadership in leading end-to-end, cross-functional projects from ideation through deployment.
• Ability to influence technical direction and decision-making in complex systems.