About the job
The Rider team is the centerpiece of the Uber consumer experience, owning the core flagship app used by over 100M+ monthly active users. The Personalization team is looking for a Senior Scientist who can help us innovate on our AIML efforts and tackle the next frontier of challenges, including ranking & recommender systems, and agentic applications. As a Scientist, you will play a critical role in enhancing the personalization AIML experience for millions of Rider app users worldwide. You will leverage your expertise in machine learning and data science to optimize our ranking & recommendation systems, help develop agentic applications, and simulate marketplace outcomes, ultimately improving user satisfaction and achieving business growth through one of the most impactful channels.
Responsibilities
Prototype new models and methodologies (e.g. evidential deep learning, reinforcement learning, listwise ranking) for improving performance across the various personalization AI/ML surfaces of the rider app.
Deep dive the data and optimize current ranking algorithms to enhance the relevance and accuracy of ranking results.
Design experiments and offline simulations to measure the performance of AIML systems, including agentic applications, interpret the results, and make impactful recommendations for areas of development
Work closely with product managers, engineers, and other scientists to define project goals and deliver data-driven solutions.
Stay current with the latest advancements in personalization AIML
Mentor and review the work of junior team members
Qualifications
Minimum
M.S. or Bachelor's degree in Computer Science, Statistics, Economics, Mathematics, Operations Research, or other quantitative fields.
5+ years of industry experience as an Applied Scientist, Research Scientist, or equivalent.
Proficiency in programming languages (Python, PySpark, SQL) and ML frameworks (TensorFlow, PyTorch, Scikit-Learn),
Strong business and product sense: ability to find business opportunities in AIML Systems and help define product and engineering roadmaps to capture these opportunities.
Preferred
Prior experience with feed ranking, recommender systems, or search algorithms
Solid understanding of MLOps practices, including design documentation, testing, and source code management with Git.
Advanced skills in the development and deployment of large-scale ML models and optimization algorithms