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Staff Product Data Scientist, Workspace Collab
GoogleNew York City, United Statesfull_timeVerifiedPosted 11 Jun 2026
💰 $279,000/yr($192,000/yr – $279,000/yr)
About the role
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 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.
- 12 years of work experience using analytics to solve product or business problems, performing statistical analysis, and coding (e.g., Python, R, SQL).
About the job
The Workspace Collab Data Science team empowers product teams for Drive, Docs, Sheets, Slides, Vids (and more) through development of key metrics, data-driven insights, modeling, experimentation and other forms of causal inference to grow product adoption and AI usage.Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $192000 - $279000 (USD) + 20% bonus target + bonus + equity + 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. Proactively perform data exploration to understand user behavior and identify opportunities for improving and growing Workspace products.
- Apply technical expertise with observational data analysis, modeling, and causal inference to answer the most important product questions.
- Partner with Product, Engineering, UX 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.
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