**About the Role** We are looking for a Data Scientist to join our Identity Science team and apply a data-driven approach to scope and launch products within the Identity space. In this role, you will work to ensure seamless and secure signup and login, account management, and account linking flows for users, and use technical and business skills to drive Identity strategy across Uber. You will work with product managers, engineers and fellow scientists to shape the direction of a variety of areas within Identity. You will work on fast-moving, high-stakes problems reaching a global audience. A deep analytical passion and the ability to execute on key business priorities is a must for this role. **What the Candidate Will Need** \-\-\-\- What the Candidate Will Do ---- 1. Develop and monitor strategic metrics and understand all underlying drivers. 2. Design and run experiments to analyze the impact of product changes. 3. Deep dive into data to evaluate proposed product changes and identify opportunities for improving the user experience. 4. Collaborate with product managers and other business leaders to make decisions based on data insights and experiment results. 5. Communicate your findings to cross functional peers and management. \-\-\-\- Basic Qualifications ---- 1. Bachelors or M.S. with a degree in Statistics, Economics, Computer Science, or other quantitative fields. 2. Advanced SQL expertise. 3. Experience using Python or R. 4. 3yrs+ experience in improving products, understanding the user journey and extracting insights from data. 5. Understanding of experimental design and using statistical methods in exploratory analysis. 6. Experience with dashboarding / data visualization (i.e. Google Data Studio, Tableau, Mixpanel or similar). 7. Ability to work in a self-guided manner. 8. Superb communication and organization skills. \-\-\-\- Preferred Qualifications ---- 1. Identity (signup / login / account management / account security) experience. 2. Proficient at defining, utilizing and communicating performance metrics. 3. Proven track record of applying analytical/statistical methods to solve real-world problems using big data. 4. Data engineering / pipeline creation experience. 5. 1yrs+ experience launching multiple experiments (such as A/B experiments). 6. Linear regression, causal inference, credibility-weighted estimates. For San Francisco, CA-based roles: The base salary range for this role is USD$142,000 per year - USD$158,000 per year. For Sunnyvale, CA-based roles: The base salary range for this role is USD$142,000 per year - USD$158,000 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](https://www.uber.com/careers/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](https://forms.gle/aDWTk9k6xtMU25Y5A). 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.