Senior Research Data Scientist
Google· Internet & Cloud Services· Aug 10, 2026 · 3mo agoPosted 78 days ago
New York, NY, USA · Mountain View, CA, USA
In office
Full-time
Senior
$150K–$200K est.
5+ years exp
Before you apply
Ghost Signal:
Open 78 days.
Job description
As a part of the Meridian AI team, you will focus on revolutionizing Marketing Mix Modeling (MMM) by building an autonomous, agent-based AI product that automates the traditionally complex, high-friction model-building life-cycle.
Your mission is to democratize access to advanced media effectiveness measurement for Google's advertisers and agencies while also drastically improving modeling productivity and quality.
In this role, you will have the unique opportunity to bridge the gap between traditional data science and the next-generation AI. You will combine your deep econometric and statistical expertise with cutting-edge agentic technology to build Subject Matter Expert (SME) agents from the ground up, effectively encoding human analytical intuition into an autonomous system that scales expert-level measurement globally.
You will bring scientific and statistical methods to bear on the challenges of advertising product creation, development and improvement with a deep, data-driven appreciation for the behaviors of the end user and the ecosystem. As a Data Scientist on the Meridian AI team, you will shape the strategic technical direction of Google’s next-generation marketing analytics. In this pioneering role, you will act as the crucial bridge between classical data science and frontier AI. Your primary focus will be leading the research and development of advanced Marketing Mix Modeling (MMM) methodologies and embedding rigorous Bayesian statistics, causal inference, and machine learning into autonomous AI agents. By encoding human econometric reasoning into Large Language Model (LLM) driven workflows, you will build intelligent systems capable of solving highly complex measurement challenges from the ground up, ensuring Google remains the undisputed industry leader in automated, scalable marketing measurement.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $174000 - $252000 (USD) + 15% bonus target + equity + benefits
Learn more about benefits at Google .
• Lead the design and development of innovative measurement methodologies and products, setting the strategic technical direction for Meridian and AI development.
• Serve as the core bridge between classical data science and frontier AI by embedding rigorous Bayesian statistical frameworks into autonomous AI agents, encoding econometric reasoning, heuristic logic, and human analytical intuition into LLM-driven workflows from the ground up.
• Pioneer advanced quantitative methods by integrating cutting-edge causal inference, statistical modeling, and machine learning techniques to solve highly ambiguous and complex measurement challenges.
• Drive the research and development of next-generation Marketing Mix Modeling (MMM) methodologies, setting the technical strategy for integrating advanced econometric models into the Meridian AI product suite, ensuring Google remains at the forefront of marketing analytics.
• Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field.
• 5 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 3 years of work experience with a PhD degree.
• Experience with econometrics, machine learning and Bayesian statistics.
Minimum qualifications:
• Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field.
• 5 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 3 years of work experience with a PhD degree.
• Experience with econometrics, machine learning and Bayesian statistics.
Preferred qualifications:
• 8 years of work 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.
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