Staff Research Data Scientist, Ads Metrics

About the job

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.

Responsibilities

• 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.

Qualifications

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.