Senior Product Data Scientist, People Operations AI System

The AI/ML Foundations team is a group of engineers and data scientists tasked with building the foundations for engineering excellence and prototyping helpful, internal-facing AI/ML products.

The Data Scientist (DS) team focuses on delivering investigative insights that influence product priorities and business strategy across the organization. We combine advanced statistical methods with a deep understanding of user behavior and engineer systems to create helpful experiences for our internal stakeholders.

At Corp Eng, we build world-leading business solutions that scale a more helpful Google for everyone. As Google’s IT organization, we provide end-to-end solutions for organizations across Google. We deliver the right tools, platforms, and experiences for all Googlers as they create more helpful products and services for everyone. In the simplest terms, we are Google for Googlers.Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $163000 - $236000 (USD) + 15% bonus target + equity + benefits

Learn more about

benefits at Google

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Minimum qualifications

Bachelor's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field.

8 years of work experience using analytics to solve product or business problems, performing statistical analysis, and coding (e.g., Python, R, SQL) or 5 years of work experience with a Master's degree.

Preferred qualifications

• PhD degree in Statistics, Data Science, or a related quantitative field.
• Experience building original statistical tools for internal products and mentoring data scientists.

Responsibilities

• Drive advanced analysis to deliver critical AI system and application insights that shape long-term strategy and influence business priorities.
• Establish core metrics, measurement frameworks, and methodologies for business systems, partnering with stakeholders to align on metric instrumentation.
• Design and run feature experiments in partnership with product and engineering teams, advising on advanced metrics and statistical methodology.
• Define and maintain data collection pipelines, and lead the peer review process for statistical models across the engineering organization.
• Translate complex statistical variance into clear business risk or cost-saving narratives for executives and non-technical stakeholders.