Staff Data Scientist, Weather

Waymo is seeking a Staff Data Scientist with a focus on weather intelligence to lead the modeling of weather patterns and evaluate how adverse conditions impact driver performance. This hybrid role in Mountain View, California collaborates across data science and engineering to integrate weather data sources and strengthen measurement signals, enabling deployment and scalable improvements. The compensation range for this position is USD 251,000 - 310,000 per year.

Responsibilities

• Establish and maintain rigorous evaluation standards to ensure deployment, scaling, and mitigation decisions are informed by reliable signals; identify gaps where signals fail to scale with the business or lack actionable insights, and drive remediation.
• Develop data pipelines and predictive models that fuse a range of weather data streams (first-party, second-party, third-party) to enhance Waymo's weather intelligence and forecast capability.
• Assess driver performance under adverse weather (fog, rain, snow, ice, hail, flooding) and provide input on readiness to scale in challenging conditions; present findings to senior stakeholders.
• Create scalable, repeatable analysis frameworks that support multiple climate types across domestic and international contexts.
• Streamline operational processes for addressing adverse weather across diverse geographies to improve responsiveness and efficiency.
• Build a deep understanding of Waymo's long-term roadmap and collaborate with product, engineering, and systems teams to unlock deployment milestones; influence engineering plans to enhance measurement capabilities.
• Act as both a hands-on contributor and a technical lead for junior data scientists, framing and solving ambiguous problems by prioritizing tasks and innovating statistical methods while promoting data science excellence across the organization.

Requirements

• A degree in a quantitative field (for example Statistics, Mathematics, Physics).
• PhD in a quantitative area with 8+ years of industry experience, or 12+ years solving data science problems in industry.
• Experience building and deploying models for spatio-temporal data.
• Proven experience as a technical lead.
• Experience collaborating in highly cross-functional teams and fostering a data-driven culture.
• Expertise in advanced statistical methods applied to real-world problems; familiarity with ML systems and models.
• Demonstrated proficiency with data analysis libraries and packages in Python, R, and SQL.

Technologies

• Python
• R
• SQL

We Prefer

• PhD in a quantitative field related to weather (Atmospheric Science, Meteorology, Hydrology, etc.) or substantial academic work applying statistical methods in these domains.
• Experience integrating third-party (public/private) weather data sources alongside other data sources.
• Background solving weather modeling or forecasting problems.
• A track record of independently driving data science projects to deliver measurable business value.
• Experience with large-scale evaluation frameworks used in software development.