Staff Data Scientist, Weather

Job Summary

The successful candidate will lead initiatives for modeling weather patterns and their impact on Waymo’s ride-hailing service, and streamline operational procedures to mitigate weather-related challenges in real time. They will measure the performance of the Waymo Driver in adverse weather conditions and communicate findings to senior stakeholders.

Key Responsibilities

• Define and uphold a high bar for measurement rigor, ensuring reliable evaluation signals for deployment, scaling, and mitigation decisions.
• Identify and resolve gaps in current evaluation signals, enhancing Waymo’s business and providing actionable insights for stakeholders.
• Build pipelines and models integrating various weather-specific data sources to improve Waymo’s weather intelligence and prediction capabilities.
• Measure the performance of the Waymo Driver in adverse/extreme weather conditions and provide input on Waymo’s readiness to scale in challenging weather contexts.
• Develop scalable and repeatable analysis frameworks supporting multiple climate types, both domestically and internationally.
• Optimize operational processes for addressing adverse/extreme weather across various geographical territories, improving efficiency and responsiveness.
• Collaborate with product, engineering, and systems engineering leads to unlock key deployment milestones and influence roadmaps for improving measurement capabilities.
• Be an opinionated partner influencing engineering work to enhance data science capabilities and champion data science excellence.
• Contribute actively to the team as a technical lead, framing and solving ambiguous problems, and innovating on statistical methods.

Requirements

• Degree in a quantitative field (Statistics, Mathematics, Physics).
• Experience working with and building models for spatio-temporal data.
• Experience as a technical lead.
• Experience working in highly cross-functional teams and championing a 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.

Preferred Qualifications

• PhD in a quantitative, weather-adjacent field (Atmospheric Science, Meteorology, Hydrology, etc.).
• Academic experience developing and applying statistical methods in those fields.
• Experience working with third-party weather data sources and integrating multiple data sources.
• Experience solving problems related to weather modeling or prediction.
• A demonstrated track record of independently driving data science projects to deliver business value.
• Experience with large-scale evaluation frameworks for software development.