Data Scientist - Waymo

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

#LI-Hybrid