The opportunity
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…
What you'll do
Define and uphold a high bar for measurement rigor. Ensure we can confidently: rely on the evaluation signals informing deployment, scaling, and mitigation 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.
Build pipelines and models integrating a range of existing and novel: weather-specific data sources (1P/2P/3P) to enhance Waymo’s weather intelligence and prediction capabilities.
Measure the performance of the Waymo driver in adverse/extreme weather: conditions (fog, rain, snow, ice, hail, flooding), providing input on Waymo’s readiness to scale in challenging weather contexts. Communicate these findings to senior stakeholders.
Develop scalable and repeatable analysis frameworks that support multiple: climate types, both domestically and internationally.
Optimize Waymo’s operational processes for addressing adverse/extreme weather: across a wide range of geographical territories, making them smarter, more responsive, and more efficient.
Develop a deep understanding of Waymo’s long-term roadmap, and collaborate: with leads in product, engineering, and systems engineering to unlock key deployment milestones. Be an opinionated partner influencing roadmaps for engineering work to improve our measurement capabilities.
What they're looking for
- Be an active technical contributor on the team, as well as a technical lead: to junior 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.
- Degree in a quantitative field (e.g. Statistics, Mathematics, Physics).
- Either a PhD in a quantitative field and 8+ years of industry experience, or: 12+ years of industry experience solving data science problems.
- Experience working with and building models for spatio-temporal data.