Senior Staff Research Data Scientist, Search Ads in AI Experiences

Socket.dev; Mountain View (CA); On-site; USD 262,000 - 364,000; Part time

Job summary

Google is hiring for a lead data science role within the Ads in AI Experiences team to reinvent Search Ads for the AI era. The role focuses on building models and analytics to drive new, AI-powered advertising experiences and business value.

The team works across the stack—from data infrastructure to user experience and business models—while collaborating with stakeholders to translate questions into actionable analysis and measurable impact. Expect a highly technical, fast-paced environment.

Qualifications

• Master's degree in statistics, data science, mathematics, physics, economics, OR engineering or related quantitative field.
• 10 years of work experience using analytics to solve product or business problems, or 8 years with a PhD.

Responsibilities

• Collaborate with stakeholders to translate business questions into tractable analysis and metrics.
• Design and evaluate models to solve defined problems with limited precedent.
• Gather information on goals, priorities, and data infrastructure for analysis.
• Extract, curate, and validate data across sources using SQL, R, Python to ensure analysis readiness.

Skills

Analytics problem solving

Statistical thinking

Education

Master's degree in quantitative field

PhD degree (alternative)

Tools

Python

R

SQL

Job description

Minimum qualifications:

• Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field.
• 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.

Preferred qualifications:

• 12 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 10 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: $262000 - $364000 (USD) + 25% bonus target + equity + benefits

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.