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Senior Staff Data Scientist, Workspace AI Foundations

Google Sunnyvale, California, United States · $240K–$333K/yr

Software Development · 10,001+ employees

8 h ago
data-scientist Principal (10+ yrs) Full-time United States
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About the role

Lead horizontal metric and evaluation frameworks to quantify system quality and productivity gains across GenAI models and agentic tooling. Deliver executive-ready insights to guide product roadmaps and define scalable data models and telemetry standards.

What they look for

Data Science Statistics Python R SQL GenAI Product Strategy Model Evaluation Data Architecture Experimentation Causal Inference Telemetry ETL Pipelines Data Modeling Machine Learning

Requirements

Requires a Bachelor's degree in a quantitative field and at least 13 years of relevant work experience in analytics and coding. Candidates must demonstrate expertise in statistical analysis, experimentation methodologies, and building data pipelines.

Benefits

Bonus Equity Health Insurance

Full description

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).

About the job:

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. Responsibilities:

  • 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.

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