Staff Data Scientist

AI Chopping Block, Inc.; Zürich; Hybride; CHF 170 000 - 210 000; Plein temps

Résumé du poste

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. The Ads Metrics team develops mechanisms, experiment designs, evaluation metrics, and analysis libraries to support the next generation of 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

Qualifications

• Master's degree or PhD in a quantitative field or equivalent practical experience.
• 8 years of analytics experience or 6 years with a PhD.

Responsabilités

• Collaborate with engineering and product partners to identify key questions and translate them into experiments, analysis, metrics, or models.
• Architect end-to-end analysis pipelines translating ambiguous 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.
• Translate system telemetry into statistically sound evidence ensuring data integrity for research-grade analysis that supports billion-dollar launches.

Connaissances

Experiment design

Causal inference

Time-series analysis

Statistical modeling

Data visualization

Formation

Master's degree in a quantitative field

PhD degree in a quantitative discipline

Outils

Python

R

SQL

Description du poste

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 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.