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Waymo aims to scale its autonomous ride-hailing service and achieve its mission of becoming the world's most trusted driver by ensuring the Waymo Driver meets strict standards for safety, compliance, and driving and service quality.

Requirements

• Degree in a quantitative field (e.g. Statistics, Mathematics, Physics)
• 5+ years of industry experience solving data science problems, or a PhD in a quantitative field and 3+ years of industry experience
• 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
• PhD in a quantitative field
• A demonstrated track record of independently driving data science projects to deliver business value
• Experience solving problems related to Autonomous Driving or Ride Hailing

Responsibilities

• 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 by scoping technical priorities and innovating on statistical methods.
• Derive data-driven conclusions and communicate findings to senior stakeholders.
• Establish yourself as the point-of-contact for a significant project area by using data to drive technical decisions and demonstrate success.
• Collaborate with Product and Engineering partners developing the Waymo Driver and Waymo’s simulation software; facilitate deployment readiness decisions for both products.

Other

• Mentor other data scientists and provide constructive technical feedback within the team and across Waymo.
• Collaborate with Product and Engineering partners developing the Waymo Driver and Waymo’s simulation software; facilitate deployment readiness decisions for both products.
• Derive data-driven conclusions and communicate findings to senior stakeholders.
• Establish yourself as the point-of-contact for a significant project area by using data to drive technical decisions and demonstrate success.
• Report to a data science manager.