Responsibilities

• Drive the development of measurement roadmap to solve complex data questions for EMEA campaigns.
• Define data requirements, frameworks, and regional strategy to scale media measurement efforts.
• Set and achieve ambitious measurement goals aligned with global marketing objectives.
• Drive AI transformation to enhance productivity, creativity, and innovation, while upholding Google's AI Principles.
• Oversee the analysis and interpretation of high-dimensional datasets to deliver actionable results.
• Act as a domain expert to review statistical analyses, methodologies.
• Partner with cross-functional leadership (including Media CoE, Finance, Product Areas) to guide marketing budget allocation, answer complex statistical questions, and ensure decision-making is aligned with objectives.
• Manage and coach a high-performing team of Business Data Scientists and Analysts.
• Advocate knowledge sharing, collaboration, and solution reuse across Google’s broader marketing analytics and data science communities.

Requirements

• Master's degree in a quantitative discipline such as Statistics, Engineering, Sciences, or equivalent practical experience.
• 9 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis.
• 3 years of experience in people management, in leading, mentoring, and developing technical and analytical teams.
• Experience writing, reviewing, and maintaining code for data manipulation and analysis using programming languages (such as Python, R, SQL).
• Experience presenting to non-technical audiences.

Preferred

• PhD in a quantitative fields such as Statistics, Mathematics, Economics, Operations Research, Data Science.
• Experience in advanced marketing measurement methodologies (e.g., conversion lift, geo-experiments, Media Mix Modeling, brand lift studies) for a major regional or global brand.
• Experience collaborating cross-team to define data requirements, analyze product metrics, resolve data gaps.
• Proficiency managing large, complex datasets.
• Knowledge of statistics (e.g., causal inference, predictive modeling, survey methodology) to solve multi-dimensional, complex business problems.
• Excellent communication, executive-influence skills, with an ability to translate complex quantitative findings into actionable insights for executive leaders.