About the job

Empower the Google Workspace ecosystem with intelligent capabilities including Gmail, Docs, and Meet by delivering actionable insights and defining the critical metrics that drive quality and high-impact organizational opportunities.

The US base salary range for this full-time position is $262,000-$365,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.

Responsibilities

• Provide high-level technical direction for the Workspace Data Science (WDS) GenAI Foundations team, fostering a culture of rapid experimentation and leading the organization through complex technical transitions.

• Utilize AI models and tools as integral components for evaluating, synthesizing, and understanding complex datasets.

• Develop new methodologies to improve the performance of Google's models through better training data, including data acquisition, and insights.

• Drive Data Science-led horizontal experimentation and evaluation across key components of the Workspace GenAI Platform.

• Act as a technical partner, collaborating closely with Research, Engineering, and Product teams (Workspace and Google Deep Mind).

Minimum qualifications

• Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field.

• 10 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 8 years of work experience with a PhD degree.

Preferred qualifications

• 12 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 10 years of work experience with a PhD degree.