About the Role
The Reserve product is a maturing product, most of the “low hanging fruit” initiatives to improve the experience to consistently deliver high reliability have been delivered. Currently, our focus is in building smart levers to maintain reliability while driving efficiency, improving our unit economics and growing adoption and retention.
Some of the key projects involve major changes in Pricing, Matching and Dispatch that involve transitioning from heuristic based decision making to building machine learning models to make key decisions in real time, as well as important changes to Driver and Rider experience, including discoverability and awareness generation levers, and finally piloting new use cases that leverage the Reserve Tech as a platform to unlock new vectors of growth.
What the Candidate Will Need / Bonus Points
What the Candidate Will Do
- Work together with Product, Operations, and Engineering partners to design a roadmap of features and initiatives as well as the long-term team strategy
- Run large scale experiments, observational studies, deep dives into business and user behavior trends and present findings to technical and non-technical audiences
Basic Qualifications
- Ph.D., M.S. or Bachelor's degree in Statistics, Mathematics, Computer Science, Machine Learning, Operations Research, or other quantitative fields.
- 2+ years of industry experience as an Applied or Data Scientist or equivalent (not required with Ph.D.).
- Proficiency in programming languages (Python, Java, Scala) and ML frameworks (TensorFlow, PyTorch, Scikit-Learn), underpinned by a solid grasp of MLOps practices, including design documentation, testing, and source code management with Git.
- Good understanding of experimental design and analysis (e.g., A/B and market-level experiments), causal inference.
- Good business and product sense: delight in shaping vague questions into well-defined analyses and success metrics that drive business decisions.
Preferred Qualifications
- Ability to drive clarity on the best analytic solution for a business objective
- Expertise in causal inference, A/B testing designs, multivariate testing, and other advanced analytical methods.
- Experience in designing highly scalable, resilient systems for customer-facing applications and familiarity with optimization techniques.
- Propose, design, and analyze large scale online experiments and interpret the results to draw detailed and actionable conclusions.
- Collaborate with cross-functional teams across disciplines such as product, engineering, operations, and marketing to drive system development end-to-end from ideation to productionization
For Seattle, WA-based roles: The base salary range for this role is USD$161,000 per year - USD$179,000 per year.
You will be eligible to participate in Uber's bonus program, and may be offered an equity award & other types of comp. All full-time employees are eligible to participate in a 401(k) plan. You will also be eligible for various benefits. More details can be found at the following link https://jobs.uber.com/en/benefits.
Uber's mission is to reimagine the way the world moves for the better. Here, bold ideas create real-world impact, challenges drive growth, and speed fuels progress. What moves us, moves the world - let's move it forward, together.
Uber is proud to be an Equal Opportunity 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.