Minimum qualifications:

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

Preferred qualifications:

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

About the job

Google Workspace is a smart, simple, and secure suite of productivity apps—including Gmail, Docs, Drive, Calendar, Sheets, Meet, and Chat. Designed to simplify workflows and boost team productivity, Workspace features real-time collaboration at its core, allowing information to flow freely across devices and teams so that great ideas are never lost.

As a Tech Lead Manager on the Workspace Monetization Data Science team, you will play a pivotal role in scaling the business and impacting billions of users worldwide. In this role, you will partner closely with product and engineering teams to shape monetization and growth strategies.

As a manager and technical leader, you will deliver data-driven insights to guide product roadmaps, advance analytics infrastructure, and elevate our data foundation. Additionally, you will lead a high-performing team of data scientists, defining vision and priorities while advocating for a culture of collaboration, growth, and excellence.

Responsibilities

• Act as a thought partner to engineering and product Management leads, providing data-driven perspectives on product direction and strategic opportunities. Mentor and develop data science talent.
• Conduct deep-dive analyses to identify high-impact opportunities for growing Workspace revenue and user value. Define and align the data science team’s vision and priorities, balancing short-term execution with long-term strategic investments.
• Communicate complex findings and recommendations clearly and effectively to technical and non-technical stakeholders, including executive leadership.
• Own end-to-end project outcomes, covering problem definition, metrics development, data extraction and manipulation, visualization, and statistical modeling. Contribute to the development of analytics infrastructure and elevate the overall data foundation.
• Navigate ambiguous or unprecedented challenges by framing strong hypotheses and delivering recommendations that combine analytical accuracy with product expertise.