- Robot type
- Autonomous Vehicle
- Location
- San FranciscoCaliforniaUSA
- Job type
- Business Operations
- Posted
- Aug 31, 2026
2027 Summer Intern, MS/PhD, Data Science - Commercialization Testing
Job description
Waymo's Systems Engineering team works together to blend software and hardware systems in groundbreaking new ways. We set the high performance standards that ensure our vehicles run smoothly and keep passengers safe, then design and perform the tests that validate that performance. We're looking for talented teammates who’ll help us maintain strong teamwork and are passionate about driving results.
Waymo interns partner with leaders in the industry on projects that create impact to the company. We believe learning is a two-way street: applying your knowledge while providing you with opportunities to expand your skill-set.
Waymo is an autonomous driving technology company with the mission to…
Job responsibilities
- Develop, implement, and refine Bayesian forecasting models to interpret small-sample test results.
- Build and optimize predictive analytics models to monitor fleet operations and analyze vehicle behavior.
- Collaborate with Test Engineers to design experimental procedures on closed courses and simulation environments.
- Extract, process, and analyze large-scale, high-dimensional datasets from vehicle logs and depot operations using Python and SQL.
- Partner with Systems Engineering, Software Engineering, and Product teams to determine key performance characteristics for model coverage.
Job requirements
- Enrolled in a graduate program (Master's or PhD) in Data Science, Statistics, Operations Research, Civil Engineering (with a focus on traffic/mobility analytics), or a highly quantitative field.
- Strong foundation in predictive analytics, Bayesian modeling, and statistical inference.
- Proficiency in writing production-quality Python.
- Strong SQL skills with experience querying and synthesizing data from large-scale databases.
- Strong communication skills to present complex quantitative results and statistical limitations clearly to non-technical stakeholders.
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