Staff Product Data Scientist, Merchant Shopping

Staff Product Data Scientist, Merchant Shopping Share Staff Product Data Scientist, Merchant Shopping _corporate_fare_ Google _place_ Mountain View, CA, USA **Advanced** Experience owning outcomes and decision making, solving ambiguous problems and influencing stakeholders; deep expertise in domain. Share Staff Product Data Scientist, Merchant Shopping **Minimum qualifications:**

• Bachelor's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field.
• 10 years of work experience using analytics to solve product or business problems, performing statistical analysis, and coding (e.g., Python, R, SQL) or 8 years work experience with a Master's degree.
• Experience with machine learning (ML), machine learning algorithms, artificial intelligence (AI) algorithms, coding, system design, or software development.

**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** Help serve Google's worldwide user base of more than a billion people. Data Scientists provide quantitative support, market understanding and a strategic perspective to our partners throughout the organization. As a data-loving member of the team, you serve as an analytics expert for your partners, using numbers to help them make better decisions. You will weave stories with meaningful insight from data. You'll make critical recommendations for your fellow Googlers in Engineering and Product Management. You relish tallying up the numbers one minute and communicating your findings to a team leader the next. We are looking for a Staff Data Scientist to lead the technical evolution of our data systems across Google’s merchant and communication products. This role focuses on Business Agents, Google Business Profiles, and Business Messaging -- surfaces that connect consumers with millions of businesses daily. In this role, you will be expected to operate fluidly between data science and software engineering. You will not just run analyses; you will build the systems that perform them. We are moving 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. Ultimately, your technical work must drive measurable business impact. You will 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. Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $192000 - $278000 (USD) + 20% bonus target + equity + benefits Learn more aboutbenefits at 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.
• Write and deploy production code to build data pipelines for Business Messaging and Business Agents.
• Structure our data warehouses so 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. 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. Write clean, maintainable code (Python, SQL), and mentor executive team members on system architecture and advanced statistical methods.

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