About the Role
End-to-end product development from a data, analytics, modeling and experimentation perspective. Specifically, formulating ambiguous business problems, prototyping various machine learning & optimization & causal inference & marketplace dynamics models, producing data insights, and designing & analyzing experiments.
About the Team**(We are hiring for multiple teams)**
We are building the future of Uber's mobility and logistics platforms. Our teams drive innovation across critical areas, including:
- Delivery Marketplace: A central pillar of Uber’s delivery products, serving as the "brain" of the operation. We drive every decision that enables orders to go from point A to point B — from Uber Eats & Grocery, to newer verticals like Uber Direct and Connect. We’re responsible for running an efficient marketplace with dispatch & pricing decisions.
- Maps: Building the geospatial technologies for all of Uber, including travel time prediction (ETA), routing, search & ranking, pickup & dropoff recommendation, and trip intelligence. The outputs power marketplace systems like pricing & matching and the core trip experience.
- Uber One Membership: Enhancing user experience and growth for Uber One, a fast-growing program providing members with exclusive benefits, best prices, and priority across the platform.
- Experimentation Platform: Building the experimentation platform and the underlying measurement models at Uber, providing reliable, trustworthy and agile experimentation and experiment analysis to power business decisions across the entire Uber ecosystem.
- Autonomous Mobility & Delivery (AM&D): Pioneering the integration of autonomous vehicles into the existing ecosystem, tackling the complex challenge of building a reliable, efficient, and scalable hybrid marketplace for both Rides and Eats.
What You’ll Do
- Solve ambiguous, challenging business problems using data-driven approaches including Causal Inference, Optimization and ML.
- Own the product development cycle end to end from data and science aspects.
- Provide thought leadership and mentorship for critical science problems across teams / orgs.
- Develop data-driven business insights and work with cross-functional customers to find opportunities and recommend prioritization of initiatives.
- Design and analyze experiments, present results that provide actionable recommendations.
- Develop and implement statistical / econometric methodologies to improve results validity, power and generalizability.
- Orient the teams around data-driven product development by driving the creation of logging, metrics, data visualization and diagnostic tools, and experimentation paradigms.
- Define how our teams measure success, by developing metrics, in close partnership with cross-functional partners.
Basic Qualifications
- Ph.D., M.S. or Bachelor's degree in Economics, Statistics, Machine Learning, Operations Research, or other quantitative fields.
- 7+ years of industry experience as an Applied or Data Scientist or equivalent (or 4+ years with Ph.D.).
- Excellent communication and collaboration skills: Able to lead initiatives across multiple product areas and communicate findings with leadership and product teams.
- Background in at least one programming language (eg. Python, R, Java, Ruby, Scala/Spark or Perl)
- Ability to use Python or similar technologies to work efficiently with large data sets and prototype algorithms & models
- Coding and SQL proficiency and ability to develop statistical analysis and algorithm prototyping in Python or R.
- Experience designing experiments and interpreting the results to draw detailed and actionable conclusions across a variety of key performance indicators
Preferred Qualifications
- Ph.D. in Economics, Statistics, Machine Learning, Operations Research or other quantitative fields.
- Experience in leading key technical projects and substantially influencing the scope and output of others
- Thought leadership to drive crossi-functional projects from concept to production.
- Experience in large-scale marketplace algorithm design or experimentation platform.
- Experience with exploratory data analysis, statistical analysis and model development
- Strong programming skills to prototype models in at least one of Python (preferably), R, Java, Go, Scala
For New York, NY-based roles: The base salary range for this role is USD$216,000 per year - USD$240,000 per year.
For San Francisco, CA-based roles: The base salary range for this role is USD$216,000 per year - USD$240,000 per year.
For Sunnyvale, CA-based roles: The base salary range for this role is USD$216,000 per year - USD$240,000 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. 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.
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.
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.