Staff Data Scientist
Posted Updated 16
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Summary
Staff Data Scientist on Google's Ads Metrics team, the data science group behind Search Ads. Day to day: design experiments, causal inference, and time-series analyses, build end-to-end analysis pipelines, and translate business questions into statistical models alongside engineers and researchers using Python, R, SQL, and AI tools like Gemini.
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.
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.
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 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.
- Collaborate with our engineering and product partners to identify key questions to answer. Translate and refine business questions into appropriate experiments, analysis, evaluation metrics, or mathematical models.
- Architect end-to-end analysis pipelines, translating ambiguous business questions into rigorous frameworks and statistical models.
- Design and evaluate complex experiments or models (e.g., causal inference, hierarchical models) to solve problems with limited precedent in retrieval and ranking.
- Translate system telemetry into statistically sound evidence, ensuring the highest standards of data integrity for research-grade analysis that unblocks billion-dollar launches.
Minimum qualifications:
- Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field, or equivalent practical experience.
- 8 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 6 years of work experience with a PhD degree.
Preferred qualifications:
- PhD degree in a quantitative discipline (e.g., Statistics, Operations Research, Economics, Computational Biology, Computer Science, Mathematics, Physics, or Engineering).
- Experience with statistical software (e.g., Python, R), database languages (e.g., SQL), and agentic development (e.g., Gemini, Antigravity).
- Demonstrated statistical data analysis and experimental design capabilities.
- Deep expertise in modern statistical theory, including experiment design, regression models, causal inference, sampling methods, time-series analysis, and hierarchical modeling.
- Strong record of scientific communication and presentation skills, with the ability to distill investigative findings for executive stakeholders.
- Strong data intuition and business acumen, with relevant experience in data analysis to solve business problems in complex, fast-moving, and ambiguous business environments.