Senior Staff Data Scientist, Workspace AI Foundations

Google•Kirkland, WA

2d•$240,000 - $333,000

About The Position

Google Workspace is a suite of productivity apps—including Gmail, Docs, Drive, Calendar, Sheets, Meet, and Chat—designed to simplify workflows and support team productivity through real-time collaboration. The Workspace team uses AI to make these products more intelligent, delivering actionable insights, defining critical metrics, measuring quality, and identifying high-impact opportunities. As a Staff/Senior Staff Product Data Scientist on the Workspace GenAI Foundations team, you will serve as a technical leader and strategic advisor at the intersection of product strategy, model evaluation, and data architecture. The role spans horizontal AI pillars including Agentic Ecosystems, AI Platform, Workspace Intelligence, and Abuse & Safety Protections. You will partner with senior Product, Engineering, and Research leaders across Workspace and DeepMind to develop measurement frameworks, experimentation methodologies, and foundational data pipelines for AI-powered productivity and agentic experiences serving over 3 billion users.

Requirements

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); alternatively, 10 years of work experience with a Master's degree.

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 horizontal metric and evaluation frameworks to quantify engagement, productivity gains, and end-to-end system quality across GenAI models, agentic tooling, context grounding, and safety.
Deliver executive-ready insights and recommendations to guide product roadmaps, organizational transitions, and resource allocation.
Analyze behavioral, telemetry, and evaluation datasets to identify adoption, quality, and reliability gaps; latency/cost trade-offs; and 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 connecting data across Workspace domains.
Drive experimentation, observational causal inference, and rigorous model evaluations using AI models and tools, as well as human and automated rating systems.