Staff Product Data Scientist, Gmail, Chat, Calendar

About the job

Help serve Google's worldwide user base of more than a billion people. Data Scientists provide quantitative support, market understanding and a strategic perspective to our partners throughout the organization. As a data-loving member of the team, you serve as an analytics expert for your partners, using numbers to help them make better decisions. You will weave stories with meaningful insight from data. You'll make critical recommendations for your fellow Googlers in Engineering and Product Management. You relish tallying up the numbers one minute and communicating your findings to a team leader the next.

The Communications & Time Management (CTM) Data Science team shapes decision-making and provides actionable insights to guide product development for Gmail, Chat, and Calendar.

As a Data Scientist/Analytics Engineer, you will work closely with product and engineering teams to build products enjoyed by 3B+ users.

Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.

• Health, dental, vision, life, disability insurance
• Retirement Benefits: 401(k) with company match
• Paid Time Off: 20 days of vacation per year, accruing at a rate of 6.15 hours per pay period for the first five years of employment
• Sick Time: 40 hours/year (increased to 69 hours/year for Seattle) including 5 discretionary sick days per instance
• Maternity Leave (Short-Term Disability + Baby Bonding): 28-30 weeks
• Baby Bonding Leave: 18 weeks
• Holidays: 13 paid days per year

Minimum qualifications:

• Bachelor's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field.
  
• 10 years of work experience using analytics to solve product or business problems, performing statistical analysis, and coding (e.g., Python, R, SQL) (or 8 years work experience and a Master's degree).

Preferred qualifications:

• Master's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field.
  
• 12 years of work experience using analytics to solve product or business problems, performing statistical analysis, and coding (e.g., Python, R, SQL).

Responsibilities

• Lead the research and development of metric frameworks that quantify user engagement and productivity. Pioneer new methodologies to evaluate the effectiveness, user adoption, and efficiency gains of GenAI and agentic tooling embedded within Workspace products.
• Act as the strategic connective tissue between product engineering and central data infrastructure teams. Rather than just identifying gaps, you will marshal resources, influence partner roadmaps, and drive complex, multi-team telemetry and infrastructure projects from inception to execution.
  
• Design and build scalable data models and analytical architecture that connect data across multiple distinct product domains, ensuring cohesion rather than isolated, product-specific silos. 
• Scope and drive initiatives to enable self-serve analytics. Design unified semantic layers, governed metric stores, and high-impact executive dashboards, while leading next-generation AI-powered self-serve tooling.