About the Role
We're looking for an Applied Scientist to join our Marketing Applied Science team to help optimize marketing communications through cutting-edge machine learning and statistical modeling. In this role, you'll be responsible for running exploratory data analysis, conducting experiments, and developing scalable models that inform decisions across Uber's global marketing efforts-accelerating both demand and supply growth in over 600 cities worldwide.
The ideal candidate would be a deeply technical scientist with expertise spanning A/B testing, machine learning, NLP/LLMs, system design, and product thinking. Strong business acumen is also key. Previous experience in marketing optimization is a plus.
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What You'll Do
- Develop production-grade models using large-scale datasets to optimize marketing performance through advanced statistical modeling, machine learning, and natural language processing techniques.
- Apply modeling approaches to address key CRM growth challenges, including creative optimization, channel mix selection, and cadence/timing optimization.
- Build models that generate insights from existing customer behavior to drive acquisition, retention, engagement, and conversions, and to scale effective prospecting strategies.
- Partner with cross-functional teams to deploy models in production and enhance analytics solutions through data-driven recommendations.
- Work with large-scale data processing tools such as Spark, Hive, and Uber's proprietary machine learning platforms.
- Collaborate closely with marketing, product, and engineering teams; participate in project planning, stakeholder alignment, and data knowledge-sharing forums.
- Translate data insights into clear, actionable recommendations that influence strategy and execution.
Basic Qualifications
- Ph.D. or M.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)
- Expertise with Python
- Advanced SQL skills - comfortable working with very large data sets.
- Experience with machine learning techniques and advanced analytics (e.g., regression, classification, clustering, time series, econometrics, mathematical optimization)
- Advanced skills in probability and statistics
Preferred Qualifications
- 1-2 years of experience as a data scientist or applied scientist, ideally at a company with global-scale operations.
- Experience with reinforcement learning, contextual bandits, natural language processing, or generative AI is a strong plus.
- Familiarity with data engineering tools and large-scale data processing technologies such as Hive, Spark, or similar frameworks.
- Strong communication skills; highly organized, collaborative, and capable of managing multiple priorities in a cross-functional environment.
- Excellent problem-solving skills and strong business acumen, with the ability to connect technical insights to strategic decisions.
For San Francisco, CA-based roles: The base salary range for this role is USD$155,000 per year - USD$172,000 per year.
You will be eligible to participate in Uber's bonus program, and may be offered an equity award & other types of comp. 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.