Research Scientist, AI and Economics

• Conceptualize, execute, and publish peer-reviewed economic research, working papers, and research blogs evaluating the effects of AI on the economy. Output form-factor may vary.
  
• Apply causal frameworks (e.g., difference-in-differences, instrumental variables, regression discontinuity, synthetic controls, quasi-experiments) to understand economic mechanisms from observational data.
  
• Pioneer new applications of Large Language Models (LLMs), natural language processing, and modern machine learning techniques to structure, classify, and extract high-fidelity economic signals from massive unstructured datasets and product logs.
  
• Bridge standard econometrics with scalable data science workflows in Python, ensuring methodological matches production-scale execution.
  
• Advise cross-functional partners in Research, GDM Google Cloud, Policy, and Product leadership on data-driven economic strategy and potential policy implications.
  

Minimum qualifications:

• PhD in Economics, Quantitative Social Sciences, Statistics, or a related quantitative field, or equivalent practical experience.
  
• 4 years of post-PhD research experience in academia, research institutes, or industry research labs.
  
• Experience with causal inference, experimental or quasi-experimental design, and applied econometrics.
• One or more first-author papers accepted at or published in economics journals or peer-reviewed AI/CS venues.
  
• Experience in Python and standard scientific computing/ML libraries such as pandas, NumPy, PyTorch/Jax, scikit-learn, statsmodels.

Preferred qualifications:

• Experience combining structural econometrics or causal models with modern machine learning pipelines to manage high-dimensional, unstructured data.
  
• Strong publication record explicitly focused on the economics of AI, digital economics, technology adoption, or labor/productivity impacts.
  
• Proven success working productively alongside software engineers, ML researchers, and policy/legal experts.
  
• Ability to translate technical econometrics and ML methodologies into intuitive, high-impact narratives for C-suite executives, policymakers, and interdisciplinary non-experts.
  

• PhD in Economics, Quantitative Social Sciences, Statistics, or a related quantitative field, or equivalent practical experience.
  
• 4 years of post-PhD research experience in academia, research institutes, or industry research labs.
  
• Experience with causal inference, experimental or quasi-experimental design, and applied econometrics.
• One or more first-author papers accepted at or published in economics journals or peer-reviewed AI/CS venues.
  
• Experience in Python and standard scientific computing/ML libraries such as pandas, NumPy, PyTorch/Jax, scikit-learn, statsmodels.