The role
From Google's own posting.
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.
We empower Workspace with AI, guiding products like Gmail, Docs, Sheets, Slides, Drive, Meet, Chat, and beyond to become truly intelligent. We deliver actionable insights, define the critical metrics, and measure quality, pinpointing the highest-impact opportunities for the entire organization.
As a Staff/Senior Staff Product Data Scientist on the Workspace GenAI Foundations team, you will act as a technical leader and strategic advisor at the intersection of product strategy, model evaluation, and data architecture across our horizontal AI pillars—spanning Agentic Ecosystems, AI Platform, Workspace Intelligence, and Abuse & Safety Protections. Partnering closely with senior Product, Engineering, and Research leaders across Workspace and DeepMind, you will pioneer the measurement frameworks, experimentation methodologies, and foundational data pipelines that shape AI-powered productivity and agentic experiences for over 3 billion users.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $240000 - $333000 (USD) + 25% bonus target + equity + benefits
Learn more about benefits at Google .
Lead horizontal metric and evaluation frameworks that quantify engagement, productivity gains, and end-to-end system quality across GenAI models, agentic tooling, context grounding, and safety.
Deliver clear, executive-ready insights and recommendations that guide product roadmaps, organizational transitions, and resource allocation.
Work on challenges across behavioral, telemetry, and evaluation datasets to identify user adoption, quality or reliability gaps, latency/cost trade-offs, and new methodologies to improve performance.
Define upstream logging and telemetry standards, design scalable data models and ETL pipelines, and build unified semantic layers and self-serve analytics that connect data across Workspace domains.
Drive experimentation, observational causal inference, and rigorous model evaluations—leveraging AI models/tools as well as human and automated rating systems.
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).
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).
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).