Staff Product Data Scientist, Google Shopping

Job description

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.

Our mission is to build scalable, commercially focused, and machine-consumable data products and insights that simplify problems and drive outcomes for merchants and consumers.

As a Staff Data Scientist, you are a technical leader who consolidates deep analytical excellence, statistical modeling, and domain expertise into highly maintainable, scalable data products. You bridge the gap between complex mathematical insights and strategic business decisions across high-impact engineering and product teams. Operating with high autonomy, you solve the organisation’s most complex, ambiguous problems rather than being confined to a single product team or silo. You demonstrate exceptional scientific quality, ensuring that our analytical methodologies are robust, reproducible, and built to last. You partner directly with Engineering and Product to actively shape our agentic AI data products in our fast-moving "one-team" model.

The various, high-velocity team of 30 spans Mountain View and London, bringing together experts in Data Science. As a senior leader in this unit, you will have a direct global impact, actively inputting to Google’s high-profile agentic commerce AI initiatives.

Users come first at Google. Nowhere is this more important than on our Advertising and Commerce team: we believe that ads and commercial information can be highly useful to our users if that information is relevant to what our users wish to find or do. Advertisers worldwide use Google Ads to promote their products; publishers use AdSense to serve relevant ads on their website; and business around the world use our products (like Google Shopping, and Google Wallet) to support their online businesses and bring users into their offline stores. We are constantly innovating to deliver the most effective advertising and commerce opportunities of tomorrow.

Responsibilities

• Goal Setting and Measurement: Set rigorous  data science  goals and translate highly undefined, ambiguous problems into concrete, structured, and auditable data and measurement frameworks.
• Data Productisation: Package complex data flows, causal inference models, and agentic workflows into sustainable, well-designed data products and reusable codebase assets that move beyond traditional dashboards..
• Strategic Advising: Act as an expert advisor to Engineering and Product Management Directors, delivering fast-turnaround, data-driven perspectives that influence product roadmaps and strategic pivots.
• System and Schema Design: Define and evaluate complex metrics (including conversational evaluation frameworks) while partnering with Data Engineering to co-design clean data schemas.
• Technical Execution: Proactively jump into high-priority, high-friction  data science  challenges, establishing fast insights and balancing with high standards for experimental design, data modeling and storytelling.

Minimum qualifications:

• Bachelor's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field.
  
• 10 years of experience using analytics to solve product or business problems, performing statistical analysis, and coding (e.g., Python, R, SQL) or 5 years of experience with a Master's degree.

Preferred qualifications:

• Master's degree in Statistics, Mathematics, Data Science , Engineering, Physics, Economics, or a related quantitative field.
• Experience taking highly ambiguous concepts and personally designing the statistical models, data structures, and code to ship them as finished, sustainable data products.
• Experience navigating e-commerce or marketplace data, with a deep understanding of user acquisition, inventory data, and merchant/consumer behavior.
• Experience with Large Language Models, causal inference, experiment design at scale, and defining evaluation frameworks for agentic workflows.
• Experience maintaining full accountability for a technical codebase, balancing speed against analytical debt and model stability.