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Data Scientist
Waymo
H1B ✓HybridmidPosted May 26, 2026
About the Role
Develop evaluation frameworks, metrics, and novel statistical methods for autonomous vehicle performance and ML models, interpreting data from simulation and on-road driving, and communicating findings to stakeholders.
Requirements
Requires a quantitative degree, 3+ years industry experience (or PhD), expertise in advanced statistical methods, ML systems, and data analysis using Python/SQL/R, to develop evaluation frameworks and novel statistical methods for autonomous vehicle performance.
Full Job Description
Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states.
Rigorous performance evaluation of the Waymo Driver is a critical part of scaling our ride hailing service and achieving Waymo’s audacious goals. Waymo data scientists work hand-in-hand with engineering teams at each stage of the software development cycle, employing statistical models and developing metrics and measurement frameworks to ensure that the Waymo Driver meets our strict standards for safety, compliance, and driving and service quality. Autonomous driving presents a new paradigm in data science: in addition to leveraging data collected on-road, we generate our own data using state-of-the-art simulation technology—resulting in denser signals and challenging new problems in estimation and experimental design.
In this hybrid role you will report to a Data Science Manager.
You will:
- Develop evaluation frameworks for autonomous vehicle performance, for large-scale ML models, and for the quality of simulation.
- Develop new metrics, interpret trends, and investigate anomalies in data from simulation and on-road driving.
- Develop novel statistical methods to handle unique aspects of AV data; e.g. rate estimation with rare events, combining real and synthetic data, etc.
- Frame and solve ambiguous problems, derive data-driven conclusions, and communicate findings to senior stakeholders.
- Collaborate with Product and Engineering partners developing the Waymo Driver and Waymo’s simulation software; facilitate deployment readiness decisions for both products.
You have:
- Degree in a quantitative field (e.g. Statistics, Mathematics, Physics)
- 3+ years of industry experience solving data science problems or a PhD in a quantitative field
- Expertise using advanced statistical methods in an applied setting; familiarity with ML systems/models
- Demonstrated knowledge of Python/SQL/R data analysis libraries and packages
We prefer:
- PhD in a quantitative field
- Experience solving problems related to Autonomous Driving or Ride Hailing
- Experience in adjacent relevant areas like Advanced Machine Learning (Deep Learning and Diffusion models), Traffic Modeling, Safety Evaluation or Prediction
The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process.
Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements.
Salary Range
$170,000—$216,000 USD