Staff Data Scientist, TLM, Driving Quality

In this hybrid role you will report to the DS Lead for Driving Quality.

You will:

• Define and uphold a high bar for measurement rigor. Ensure we can confidently rely on the evaluation signals informing deployment and scaling decisions. Identify and work to resolve any gaps where current evaluation signals are not scaling with Waymo's business or providing the necessary actionability for stakeholders.
• Develop and adapt our evaluation framework to accommodate road rules across multiple countries.
• Collaborate with leads in product, engineering, and systems engineering to align to unlock key Waymo deployment milestones. Be an opinionated partner informing roadmaps for engineering work to improve our measurement capabilities.
• Measure performance of the Waymo driver in new territories and hardware platforms, providing input on Waymo's readiness to scale in new markets. Communicate these findings to senior stakeholders.
• Be an active technical contributor on the team, as well as a technical lead and/or manager to several data scientists. Frame and solve ambiguous problems by scoping technical priorities and innovating on statistical methods. Champion data science excellence and provide constructive technical feedback within the team and across Waymo.

Do you have experience in Team management?, * Degree in a quantitative field (e.g. Statistics, Mathematics, Physics)

• Either a PhD in a quantitative field and 5+ years of industry experience, or 7+ years of industry experience solving data science problems.
• Experience as both a technical lead and a people manager.
• Experience working in highly cross-functional teams and championing data-driven culture.
• Expertise using advanced statistical methods in an applied setting; familiarity with ML systems/models
• Demonstrated knowledge of data analysis libraries and packages in Python, R, and/or SQL.

We prefer:

• 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.
• Experience with large-scale evaluation frameworks for software development.
• Experience in adjacent relevant areas like Advanced Machine Learning, 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 $238,000-$302,000 USD

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 evaluation of the Waymo Driver is a critical part of scaling our ride hailing service and achieving Waymo's ambitious goals. In this role, you will lead a data science team specializing in evaluating the Waymo Driver's ability to comply with rules of the road. Road rule-related behaviors are some of the most challenging to identify, and areas of work include understanding human baseline performance, partnering with ML engineers to improve detection capabilities, and delivering critical insight about the Driver's current capabilities and opportunities for improvement.