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
- Lead and manage a high-performing subteam of data scientists in the area of Search Ads Targeting.
- Collaborate with our engineering and product partners to identify key questions to answer. Translate and refine business questions into appropriate experiments, analyses, 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 massive-scale 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.
- 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 experience with a PhD degree.
- Experience building, managing, and leading teams of data scientists or quantitative researchers.
- Experience applying experiment design, regression analysis, and core statistics to perform data analysis that drives business analysis and decision-making.
Preferred qualifications:
- 10 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 8 years of experience with a PhD degree.
- Experience in modern statistical theory, including experiment design, regression models, causal inference, sampling methods, time-series analysis, and hierarchical modeling.
- Strong data intuition and business acumen, with the experience in data analysis to solve business problems in complex, fast-moving, and ambiguous business environments.
- Excellent communication and presentation skills, with the ability to distill complex investigative findings for executive stakeholders.