Applied Scientist II

Full time

Employment Information

Job levelMedium level

Salary$149 000 - $165 500

IndustryInternet Marketplace Platforms

Experience3+ years

LocationSan Francisco, CA, United States

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About the Role

Applied Scientists at Uber use data to improve and automate all aspects of Uber’s core rideshare and delivery products. You will be joining the Marketplace Intelligence team that heavily invests in machine learning, causal inference, experimentation, etc to optimize consumer-centric pricing experience to increase consumer engagement. The team is critical in fostering a delightful consumer pricing experience via algorithm and UI innovations!

We are looking for experienced candidates with a passion for solving new and difficult problems with data. In this role, you will be able to utilize your strong quantitative skills in the fields of statistics, operations research and/or machine learning to improve the Uber user experience as well as the overall marketplace performance.

What You’ll Do

Build statistical, optimization, and machine learning models for a range of applications in the fare policy and pricing experience space.
Design and execute product experiments and interpret the results to draw detailed and actionable conclusions.
Use data to understand product performance and to identify improvement opportunities.
Present findings to senior management to inform business decisions.
Collaborate with cross-functional teams across disciplines such as product, engineering, operations, and marketing to drive system development end-to-end from ideation to productionization.

Basic Qualifications

Ph.D., M.S. or Bachelors’s degree in Statistics, Economics, Machine Learning, Operations Research, or other quantitative fields. (If an M.S. degree, a minimum of 1+ years of industry experience required and if Bachelor’s degree, a minimum of 2+ years of industry experience as an Applied Scientist or equivalent).
Knowledge of underlying mathematical foundations of statistics, machine learning, optimization, economics, and analytics.
Experience in experimental design and analysis.
Experience with exploratory data analysis, statistical analysis and testing, casual analysis and ML model development.
Ability to use Python to work efficiently at scale with large data sets.
Proficiency in languages and tools like SQL, R, and Spark.

Preferred Qualifications

3-5 years of industry experience.
Experience in algorithm development and prototyping.
Experience in solving an ambiguous business problem in a structured and principled way.
Well-honed communication and presentation skills.

Locations: Sunnyvale, California; San Francisco, California;

For San Francisco, CA-based roles: The base salary range for this role is $149,000 per year - $165,500 per year.

For Sunnyvale, CA-based roles: The base salary range for this role is $149,000 per year - $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.

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.