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 jobThe 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
Learn more about benefits at Google .
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.
