Staff Research Data Scientist, Search Ads in AI Experiences

Socket.dev; Mountain View (CA); On-site; USD 207,000 - 300,000; Full time

Job summary

Google Ads in AI Experiences seeks a senior analytics leader to collaborate across teams on high-impact product questions. You will translate business goals into tractable analyses, design models, and drive data-driven decision making using SQL, R, and Python.

The role emphasizes building scalable analytics infrastructure, delivering measurable results, and influencing product strategy within a leading tech ecosystem.

Qualifications

• Master's degree in a quantitative field such as Statistics, Data Science, Mathematics, Physics, Economics, OR, Engineering.
• 6–8+ years of work experience with analytics or 6 years with a PhD; 8 years with a Master’s degree.

Responsibilities

• Collaborate with stakeholders to clarify business questions and translate them into tractable analyses or models.
• Design and evaluate models using data infrastructure or existing data models to solve defined problems.
• Gather business goals, priorities, and data context to drive analysis.
• Own data gathering, extraction, formatting, validation, and quality checks across sources using SQL, R, Python.

Skills

Analytics experience

Python/R/SQL

Databases querying

Education

Master's degree in Statistics/Data Science/Engineering (quantitative field)

PhD degree

Job description

Minimum qualifications:

• Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field.
• 8 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 6 years of work experience with a PhD degree.

Preferred qualifications:

• 10 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 8 years of work experience with a PhD degree.

About the job:

The Ads in AI Experiences team is responsible for one of the highest-priority initiatives at Google: reinventing Search Ads for the AI era (AI Overview, AI Mode). We are building the tech stack, the user experience, and the business model simultaneously. Our mission is to ensure that as Search experience evolves with AI, we unlock new ways for users to discover and engage with businesses through seamless, next-generation commercial experiences. We are a team of risk-takers and builders, responsible for experimenting with rapid velocity to define the next decade of Google’s business engine.Google Ads is helping power the open internet with the best technology that connects and creates value for people, publishers, advertisers, and Google. We’re made up of multiple teams, building Google’s Advertising products including search, display, shopping, travel and video advertising, as well as analytics. Our teams create trusted experiences between people and businesses with useful ads. We help grow businesses of all sizes from small businesses, to large brands, to YouTube creators, with effective advertiser tools that deliver measurable results. We also enable Google to engage with customers at scale. Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $207000 - $300000 (USD) + 20% bonus target + equity + benefits

Learn more about benefits at Google.

Responsibilities:

• Collaborate with stakeholders in cross-project and team settings to identify and clarify business or product questions to answer. Provide feedback to translate and refine business questions into tractable analysis, evaluation metrics, or mathematical models.
• Use custom data infrastructure or existing data models as appropriate, leveraging specialized knowledge. Design and evaluate models to mathematically express and solve defined problems with limited precedent.
• Gather information, business goals, priorities, and organizational context around the questions to answer, as well as the existing and upcoming data infrastructure.
• Own the process of gathering, extracting, and compiling data across sources via relevant tools (e.g., SQL, R, Python). Format, restructure, or validate data to ensure quality, and review the dataset to ensure it is ready for analysis.