Staff Product Data Scientist

Google•Boulder, CO

4h•$192,000 - $278,000

About The Position

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 Collab Data Science team shapes decision-making and provides actionable insights to guide product development for Drive, Docs, Sheets, Slides, Pics and Vids. As a Data Scientist/Analytics Engineer, you will work closely with product and engineering teams to build products part of Workspace enjoyed by 3B+ users every month.

Requirements

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 with a Master's degree).
8 years of experience with core product analytics concepts, including user engagement metrics, funnel analysis, and A/B testing infrastructure.
8 years of experience in data modeling, designing semantic layers and metric stores that power organizational consumption.
Experience operating distributed data processing engines.

Nice To Haves

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 and methodologies to evaluate the effectiveness, user adoption, and efficiency of Generative AI and agentic tooling within Workspace products.
Partner with product engineering and central data infrastructure teams across the organization to clarify ambiguity, define roles, and manage multi-team telemetry and infrastructure projects from inception to execution.
Design and build scalable data models and analytical architecture that connect data across distinct product domains, ensuring cohesion.
Scope and manage initiatives to enable self-serve analytics by designing unified semantic layers, metric stores, executive dashboards, and AI-powered self-serve tooling.