2026 PhD Applied Scientist Intern - Commerce - Trust - Safety and Support - United States

Job Description

We're looking for Ph.D. students specializing in Applied Science to intern during Fall 2026 (12 weeks). As a Ph.D. intern, you will be embedded in a product team working on solving real-world Uber problems and will have the opportunity to partner closely with other Applied and Data Scientists, Software Engineers, Product Managers, and other cross functional partners.

About the Team

The Commerce, Trust, Safety and Support (CTSS) team applies data science and analytics to drive initiatives across all core service elements, including Customer Support, Safety, Risk, Insurance, and Identity. As a member of the team you will conduct deep-dive analyses, design and analyze experiments, and support the development of machine learning models to make using our platform as smooth and magical as possible for all users. You will play an influential role in driving critical product and policy decisions. A critical aspect of this role is being very hands-on, not only in model development and prototyping, but also through deployment and launch, helping to structure projects from the initial idea through to final implementation.

What You'll Do

• Work with a mentor closely to define a business problem, scope a project, develop, and prototype the solution using data-driven approaches

• Perform deep-dive analyses to discover root causes for safety issues and changes in trends

• Present findings to leaders to inform decisions

• Support statistical and machine learning efforts including modeling, experimentation, signal processing, time series analysis, geospatial analysis, natural language processing, large language model interactions and more

• Get an opportunity to drive the implementation and scaling of developed solutions.

• Get an opportunity to utilize and learn software engineering tools/concepts, including ML web applications (Streamlit, Flask, etc.), real-time databases, big data tools (Spark, Ray), and LLM tools and frameworks (HuggingFace Transformers, LangChain) and Generative AI APIs (OpenAI, Google).

Basic Qualifications

• Current Ph.D. student majoring in Operations Research, Mathematics, Computer Science, Statistics, Machine Learning, or other related quantitative fields

• Candidates should have at least one semester/quarter left of their education after finishing the internship

Preferred Qualifications

• Strong foundation in statistics, machine learning, optimization, economics, analytics, experimental design, and causal inference, with experience applying these methods to solve complex business problems

• Experience with exploratory data analysis, statistical modeling, experimentation, and model development

• Proficiency in SQL and familiarity with programming languages such as Python and/or R

• Demonstrated problem-solving skills, analytical rigor, and a research-oriented mindset, with the ability to drive projects from ideation and experimentation through prototyping, implementation, and deployment

• Interest in operating as a full-stack Applied Scientist, taking ownership of the end-to-end solution lifecycle and collaborating closely with cross-functional technical and business stakeholders

• Excellent communication skills, independence, and strong execution, with a bias toward action and a track record of delivering results

• Interest in software engineering fundamentals and productionizing analytical solutions, including familiarity with tools and concepts such as ML web applications (e.g., Streamlit, Flask), real-time databases, distributed computing frameworks (e.g., Spark, Ray), and modern AI/LLM frameworks (e.g., Hugging Face Transformers, LangChain)

For San Francisco, CA-based roles: The base hourly rate amount for this role is USD$67.00 per hour.

For Sunnyvale, CA-based roles: The base hourly rate amount for this role is USD$67.00 per hour.

US interns are eligible for comprehensive health coverage, life and disability insurance, and additional benefits. They are also eligible for paid time off.