The Ads Metrics team is the core data science team behind Google Search Ads. We develop mechanisms, experiment designs, evaluation metrics, statistical methods, and analysis libraries to help build the next generation of our Search advertising products.
As a Data Scientist in the Ads Metrics team, you will collaborate closely with software engineers, product managers, researchers, and analysts to push the boundaries of experiment design, causal inference, and time-series analysis for business-critical launches.
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.
Minimum qualifications:
- Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field.
- 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.
- Experience with statistical data analysis and experimental design.
- Experience with regression analysis for prediction and forecasting.
Preferred qualifications:
- PhD degree in a quantitative discipline (e.g., Statistics, Operations Research, Bioinformatics, Economics, Computational Biology, Computer Science, Mathematics, Physics, or Engineering).
- Strong data intuition and business acumen, with relevant experience in data analysis to solve business problems in complex, fast-moving, and ambiguous business environments.
- Strong record of scientific communication and presentation skills, with the ability to distill complex analytical findings for executive stakeholders.
- Deep expertise in modern statistical theory, including experiment design, regression models, causal inference, sampling methods, time-series analysis, and hierarchical modeling.