Senior Staff Product Data Scientist, Merchant Shopping

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 with a Master's degree).

Preferred qualifications:

• Master's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field.
• 15 years of work experience using analytics to solve product or business problems, performing statistical analysis, and coding (e.g., Python, R, SQL).

As a part of the Senior Staff Data Scientist, you will lead the technical evolution of our data systems across Google's merchant and communication products. Your role focuses on Business Agents, Google Business Profiles, and Business Messaging surfaces that connect consumers with millions of businesses daily.

In this role, you are expected to operate fluidly between data science and software engineering. You will not just run analyses but will build the systems that perform them. You will move away from manual SQL pulls and static dashboards. Your mandate is to build self-healing data pipelines, establish an AI-readable semantic layer, and develop autonomous agents capable of running full exploratory analyses.

Your technical work must drive measurable business impact, bridge the gap between complex infrastructure and product execution turning interaction data into clear, actionable insights that dictate what we build next. Your work will directly influence merchant Return on Investment (ROI), optimize consumer messaging funnels, and ensure our AI agents deliver tangible value to the businesses that rely on Google.

Responsibilities

• Develop AI agents capable of executing end-to-end data analysis, from exploring interaction data in Business Profiles and writing queries, to diagnosing metric shifts and generating product recommendations.
• Build and deploy production-code data pipelines for Business Messaging and Business Agents.
• Structure data warehouses so that they are universally readable by internal AI systems and product surfaces, ensuring accurate, reliable, and reproducible query retrieval.
• Architect the statistical frameworks and infrastructure for A/B testing, and build automated systems that interpret experimental results, flag statistical noise, and output clear ship/no-ship recommendations.
• Serve as a technical benchmark for the data organization by writing clean, maintainable code (Python, SQL), and mentor senior team members on system architecture and advanced statistical methods.