Data Scientist Intern [Summer 2027]

In this Role, You Will…

• Formulate and build statistical and machine learning models to evaluate system performance and analyze rare, high-consequence safety events.
• Process and analyze large-scale datasets using SQL, Python, or distributed data frameworks (Spark/Databricks) to extract actionable insights on vehicle behavior and operational risk.
• Apply advanced statistical methods (e.g., Poisson processes, GLMs, Bayesian inference, point process models, or spatial-temporal modeling) to evaluate safety-critical metrics.
• Collaborate with systems engineers and safety leads to translate complex quantitative models into rigorous mission assurance decisions.

Internship Requirements

• Currently pursuing a Master's or Ph.D. in Statistics, Data Science, Machine Learning, Computer Science, or a related quantitative engineering/scientific discipline.
• Enrolled in good academic standing at an accredited college or university throughout the internship.
• Expected to graduate between Fall 2027 and Summer 2028.
• Able to commit to a full-time, 12-week internship beginning in May/June 2027.
• Ability to relocate to the Bay Area, California, for the duration of the internship.
• Interns at Zoox may not use any proprietary information they are working on as part of their thesis, any published work with their university, or to be distributed to anyone outside of Zoox.

Qualifications

• Statistical Foundations: Deep understanding of probability theory, regression, Generalized Linear Models (GLMs), hypothesis testing, and Bayesian methods. Experience or coursework in spatial statistics, space-time models, point process models, or causal inference.
• Machine Learning & Modeling: Strong fundamentals in statistical modeling and ML (supervised and unsupervised learning, model validation, and metric evaluation).
• Data Manipulation & Querying: Proficiency with basic-to-intermediate SQL and Python for data querying, data cleaning, and statistical modeling (or alternative data packages such as Spark, dplyr, or R).
• Communication & Problem Solving: Demonstrated ability to break down ambiguous operational or system questions into structured statistical problems and clearly communicate complex quantitative findings to technical stakeholders.

Bonus Qualifications

• Large-Scale Data Processing: Experience querying, pipeline-building, and modeling massive datasets using Spark, Databricks, or cloud platforms.
• Rare-Event Modeling & Domain Experience: Familiarity with autonomous vehicles, transportation network companies (TNCs), quantitative safety/risk modeling, or fraud/rare-event detection.
• AI-Assisted Workflow & Tooling: Experience utilizing AI-assisted data analysis, modern coding agents, or LLM-driven research workflows to accelerate data exploration and model development.