Job Description

About the Role

We are looking for a Machine Learning Scientist to join our Payments Machine Learning 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 optimizing transaction routing and automated retry strategies to improve payment authorization success rates and rider/eater conversion.




We are looking for experienced candidates with a passion for solving new and difficult problems with data. 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.
- What the Candidate 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.
- 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.
- Preferred Qualifications -





Experience in the Fintech / Payments industry Excellent communications skills in a cross-functional setting. Thought leadership to drive multi-functional projects from conceptualisation to productionization. * Experience developing complex software systems scaling to millions of users with production quality deployment, monitoring and reliability.