Senior Data Scientist, Product, Workspace Monetization

Key Skills

Data science techniques, Python Programming, R Programming, SQL

Job Description

Minimum qualifications:

• Bachelor's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field.
• 8 years of experience using analytics to solve product or business problems, performing statistical analysis, and coding (e.g., Python, R, SQL) or 5 years of experience with a Master's degree.

Preferred qualifications:

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

About the job

Workspace is a cloud-based productivity suite that is revolutionizing the way people communicate and collaborate with one another. It includes products like Gmail, Calendar, Meet, Docs, Sheets, Slides, Drive and many more. It serves over 3 billion active users across the globe, it shapes people’s experience at home, school and work.

In this role, you will be responsible for delivering data-driven insights to help with product strategy and decision focused on growing the customer base and identifying opportunities to increasing business.

The US base salary range for this full-time position is $156,000-$229,000 + 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

• Define, own and evolve product success metrics. Report, analyze and forecast trends of key product metrics and make recommendations to improve them.
• Lead the design, analysis, and interpretation of product experiments.
• Apply technical expertise with observational data analysis, modeling or causal inference to answer the most important product questions.
• Partner with Product, Engineering, and cross-functional teams to influence, prioritize and support product strategy.
• Deliver effective presentations of data-driven insights and recommendations to multiple levels of stakeholders.