**About the Role** We are looking for an Applied Scientist to join the Paid Marketing Measurement team. In this role, you will work closely with multiple stakeholders, including the Marketing Channels team and the Finance team, to leverage data and advanced analytics to drive business results. An ideal candidate would have a good balance of experimentation expertise and statistical model, with a deep understanding of one of the areas. Previous experience in advertising measurement or in a similar field is a plus. **What the Candidate Will Need** \-\-\-\- What the Candidate Will Do ---- - Be in charge of productionizing models that estimate the marginal effectiveness of multiple marketing channels. - Work with the Finance team to develop data-driven models to optimize marketing budgets and ensure efficient allocation of resources. - Collaborate with the Product and/or Engineering teams to make the necessary data available (when applicable). - Collaborate with the Marketing Channels team to design and implement A/B tests to measure the effectiveness of new campaigns. - Support any ad hoc analysis required to design a robust experiment. - Leverage experiment results to help the Marketing Channels team improve the performance of their campaigns. - Partner with internal customers, including Operations, Finance, Product, and the Channel team, to develop paid marketing strategies. - Collaborate with other science teams in marketing and other organizations to improve Uber’s measurement solutions. - **Here is what the typical day would look like**: - 50% modeling, designing experiments or measurement solutions - 30% deep-dive analysis on campaign performance - 20% stakeholder meetings \-\-\-\- Basic Qualifications ---- - PhD, M.S. or Bachelors degree in Math, Economics, Bioinformatics, Statistics, Engineering, Computer Science, or other quantitative fields. - Knowledge of underlying mathematical foundations of statistics, statistical modeling, and experimentation - Proficiency in SQL - Ability to use Python or R to work efficiently at scale with large data sets - Experience with big data using technologies like Spark and Hive \-\-\-\- Preferred Qualifications ---- - If M.S. degree, a minimum of 2+ years of industry experience required and if Bachelor’s degree, a minimum of 4+ years of industry experience as a Data Scientist or equivalent - Drive to learn complex topics quickly - Previous experience in advertising tech and/or working with product and engineering teams to build scalable measurement solutions - Experience with production statistical or ML models For New York, NY-based roles: The base salary range for this role is USD$149,000 per year - USD$165,500 per year. For San Francisco, CA-based roles: The base salary range for this role is USD$149,000 per year - USD$165,500 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](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](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.