About the role
We’re looking for an Applied Scientist to join our Brand Science team to provide strategic, actionable insights around how to measure and optimize a large brand marketing budget. In the role, you’ll be part of and collaborate with a cross-functional team consisting of marketing/media stakeholders, our in-house research and insights team, as well as other scientists across the broader organization.
Problems we’re working on include:
- How do we measure the impact of advertising on television and billboards?
- Can we find a better way to allocate our brand budget across a diverse set of advertising platforms, marketing initiatives, and audiences?
- How can we improve our existing measurement tools to improve sensitivity and reduce bias?
The ideal candidate is both technically deep and intellectually humble. You enjoy out-of-the-box thinking, and are not afraid to roll up your sleeves to get things done. You prioritize practical solutions over novelty but stay curious about new methods and their tradeoffs. You focus on tangible progress and results, but also have a generous spirit, giving others the benefit of the doubt and actively seeking to understand their perspectives.
What you’ll do
- Apply statistical/econometric methods to estimate the incremental impact of changes in marketing and product strategy on brand-relevant KPIs like sentiment, awareness, and purchase intent – as well as on business outcomes like trips/orders and gross bookings.
- Build and improve technical tools for marketing problems. Examples include media mix models, statistical estimators to gauge the impact of various interventions (experimental or otherwise), optimization engines that determine how to best distribute resources, etc.
- Interface closely with external partners (media agencies, advertising platforms, measurement vendors) to deeply understand the existing capabilities and limitations of their platforms and solutions, and to push the envelope on what is possible.
- Collaborate with our in-house media, research, product, and operations teams to develop actionable customer insights and recommendations to guide high level business and marketing strategy.
Basic Qualifications
- Ph.D., M.S., or B.S. degree in Statistics, Computer Science, Applied Mathematics, Electrical Engineering, Economics, or other quantitative fields is preferred. (If M.S. degree, a minimum of 2+ years of industry experience required, if B.S. degree a minimum of 3+ years of industry experience required)
- Advanced skills in SQL, Python, and BI tools for data analysis/visualization
- In-depth understanding of and experience with experimental design
Preferred Qualifications
- Experience with solving quantitative problems in the marketing/AdTech space
- PhD in a quantitative field such as statistics, economics, or operations research
For New York, NY-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.
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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.