Research Data Scientist, Search Ads in AI Experiences

Socket.dev; Mountain View (CA); On-site; USD 147,000 - 210,000; Full time

Benefits offered by this job

Equity

Bonus target

Benefits

Job summary

Google is seeking a data analytics leader for the Ads in AI Experiences team in Mountain View, CA. You will collaborate with cross-functional teams to redefine how ads work with AI, building robust models and analytics to support decision making and business growth.

The role emphasizes translating complex questions into actionable metrics, leveraging SQL, Python, and R, and delivering high-impact analyses with strong stakeholder partnership.

Qualifications

• Master's degree or equivalent practical experience in a quantitative field.
• 3 years of work experience using analytics to solve product or business problems, including coding and data querying.

Responsibilities

• Collaborate with stakeholders to translate business questions into tractable analyses and metrics.
• Design and evaluate models using data infrastructure and existing data models to solve defined problems.
• Gather goals, priorities, and data context to inform analysis and data preparation.
• Gather, extract, and validate data across sources using SQL, Python, or R to ensure data quality.

Skills

Analytics experience

Python/R/SQL coding

Education

Master's degree in quantitative field or equivalent

Tools

SQL

Python

R

Job description

Minimum qualifications:

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

Preferred qualifications:

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

About the job:

The Ads in AI Experiences team is responsible for a high priority initiative 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 generation 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: $147000 - $210000 (USD) + 15% bonus target + equity + benefits

Responsibilities:

• Collaborate with stakeholders in cross-projects 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, using 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). Independently format, re-structure, and/or validate data to ensure quality, and review the dataset to ensure it is ready for analysis.