Uber Marketplace, specifically Rider Pricing & Incentives, aims to drive revenue growth, ridership growth, and Uber's profitability through advanced machine learning and data science by optimizing rider pricing and promotions.
Requirements
- Proficiency in one or more programming languages (e.g., C, C++, Java, Python, Go).
- Experience with machine learning and optimization algorithms.
- Experience solving complex business problems by translating them into machine learning and optimization solutions.
- Familiarity with large-scale data systems (e.g., Spark, Hive) and experience building production-ready algorithmic systems.
- Strong background in deep learning, generative AI, causal modeling, and reinforcement learning.
Responsibilities
- Take a lead on machine learning problems, working on rider pricing and promotions to develop and implement new machine learning and optimization techniques powering billions of rides around the world, and helping riders achieve their mobility needs.
- Improve the performance of models and algorithms powering pricing algorithms and promotion targeting.
- Own the problem E2E, including working with cross-functional teams to define the product and/or technical roadmap.
- Mentor more junior team members by role modeling ML best practices.
- Collaborate with cross-functional teams to ensure alignment and drive Uber’s ridership and revenue growth.
- Help Uber’s end-users by making mobility options accessible and affordable.
- Apply advanced machine learning technologies—including deep learning, generative AI for personalized communications, causal modeling, and reinforcement learning—to optimize pricing strategies and promotional systems.
Other
- Masters degree in Computer Science, Engineering, Mathematics, or a related field, with 5+ years of full-time engineering experience.
- PhD in Computer Science, Engineering, Mathematics, or a related field, with 2+ years of full-time engineering experience.
- Unless formally approved to work fully remotely, Uber expects employees to spend at least half of their work time in their assigned office.
