Senior Staff Research Data Scientist, Search Ads in AI Experiences

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 at the forefront of AI innovation, applying cutting-edge machine learning and Generative AI models like Gemini to power a multi-billion dollar global business.

Our work directly impacts billions of users by protecting users from harm, improving ad quality, and optimizing campaigns for advertiser return-on-investment. We foster a culture of deep collaboration, partnering closely with teams like Google Research and DeepMind to solve complex challenges. Join us to work on state-of-the-art AI, take on problems at an unparalleled scale, and build the next generation of advertising technology.

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

Learn more about benefits at Google [https://www.google.com/about/careers/applications/benefits/].

• 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.

• 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.

• 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.