About the Role
The Rider team's goal is to help Uber become the one-stop shop for everyone to Go Anywhere and Get Anything. We build magical, personalized experiences and help our customers discover new and relevant services, serving over 100M+ monthly active users.
The Rider Personalization team is looking for a Senior Scientist who can help us innovate on our ranking algorithms and tackle the next frontier of challenges, including marketplace optimization, simulation / forecasting, and LLM-powered model explainability.
As a Scientist, you will play a critical role in enhancing the ranking experience for millions of Rider app users worldwide. You will leverage your expertise in data analysis, machine learning, and statistical modeling to drive insights and optimize ranking algorithms, ultimately improving user satisfaction and achieving business growth through one of the most impactful levers in the Rider app.
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What the Candidate Will Need / Bonus Points
---- What the Candidate Will Do ----
- Conduct thorough analyses of large datasets to identify trends, patterns, and opportunities for improving ranking performance.
- Design, implement, and optimize ranking algorithms to enhance the relevance and accuracy of ranking results.
- Mentor and lead junior ICs and make meaningful contributions across various surfaces of the Rider app
- Generate actionable insights from data and communicate findings to stakeholders across the organization.
- Design experiments and interpret the results to draw detailed and impactful conclusions.
- 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 data science, machine learning, and feed ranking technologies.
- Define how our teams measure success, by developing metrics, in close partnership with cross-functional partners.
---- Basic Qualifications ----
- M.S. or Bachelor's degree in Statistics, Economics, Mathematics, Computer Science, Machine Learning, Operations Research, or other quantitative fields.
- 5+ years of industry experience as an Applied or Data Scientist or equivalent.
- Proficiency in programming languages (Python, Java, Scala) and ML frameworks (TensorFlow, PyTorch, Scikit-Learn),
- Experience in large-scale experiment design (e.g., A/B and market-level experiments)
- Strong business and product sense: ability to shape vague questions into well-defined analyses and success metrics that drive business decisions.
---- Preferred Qualifications ----
- Prior experience with feed ranking 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
For San Francisco, CA-based roles: The base salary range for this role is USD$183,000 per year - USD$203,000 per year.
You will be eligible to participate in Uber's bonus program, and may be offered an equity award & other types of comp. You will also be eligible for various benefits. More details can be found at the following link https://www.uber.com/careers/benefits.
Uber is proud to be an Equal Opportunity/Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you have a disability or special need that requires accommodation, please let us know by completing this form.
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.