About the Role
Uber Eats is seeking a highly skilled and motivated Scientist to join our Search Team. As a Scientist, you will play a critical role in enhancing the search experience for millions of Uber Eats users worldwide. You will leverage your expertise in data analysis, machine learning, and statistical modeling to drive insights and optimize search algorithms, ultimately improving user satisfaction and operational efficiency.
What you will do
- Conduct thorough analyses of large datasets to identify trends, patterns, and opportunities for improving search performance.
- Design, implement, and optimize search algorithms to enhance the relevance and accuracy of search results.
- 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 search technologies.
- Define how our teams measure success, by developing metrics, in close partnership with cross functional partners.
Want more jobs like this?
Get Science and Engineering jobs delivered to your inbox every week.
Basic Qualifications
- M.S. or Bachelor's degree in Statistics, Economics, Mathematics, Computer Science, Machine Learning, Operations Research, or other quantitative fields.
- 7+ 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),
- 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
- Experience in developing causal inference methodologies and experimental 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
- Ph.D., M.S. or Bachelor's degree in Statistics, Economics, Mathematics, Computer Science, Machine Learning, Operations Research, or other quantitative fields.
- Expertise in developing causal inference methodologies, experimental designs, and advanced analytical methods.
- Experience in building consumer-facing products in a technology company.
- Building Machine Learning and Deep Learning models.
- Experience managing projects across large, ambiguous scopes and driving initiatives in a fast moving, cross-functional environment.
- Experience guiding and mentoring other Scientists.
- Experience synthesizing data analyses into clear insights to influence product direction.
For San Francisco, CA-based roles: The base salary range for this role is USD$203,000 per year - USD$225,500 per year.
For Sunnyvale, CA-based roles: The base salary range for this role is USD$203,000 per year - USD$225,500 per year.
For all US locations, 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.