Research Scientist, AI and Economics

As an organization, Google maintains a portfolio of research projects driven by fundamental research, new product innovation, product contribution and infrastructure goals, while providing individuals and teams the freedom to emphasize specific types of work. As a Research Scientist, you'll setup large-scale tests and deploy promising ideas quickly and broadly, managing deadlines and deliverables while applying the latest theories to develop new and improved products, processes, or technologies. From creating experiments and prototyping implementations to designing new architectures, our research scientists work on real-world problems that span the breadth of computer science, such as machine (and deep) learning, data mining, natural language processing, hardware and software performance analysis, improving compilers for mobile platforms, as well as core search and much more.

As a Research Scientist, you'll also actively contribute to the wider research community by sharing and publishing your findings, with ideas inspired by internal projects as well as from collaborations with research programs at partner universities and technical institutes all over the world.

As a Research Scientist, you will conduct empirical research within the agenda of the AI and Economy team. You will develop novel methodologies that leverage internal product logs, unstructured text, internal datasets, and public economic indicators to measure the adoption, productivity effects, labor market transformations, and broader surplus generated by AI technologies.

This is a high-visibility individual contributor role designed for a researcher with an academic background who thrives on solving ambiguous empirical problems, publishing foundational research, and translating complex economic insights for multiple audiences.

The Technology & Society organization connects research, people, and ideas across Google and Alphabet to help shape and advance our most ambitious technology innovations and initiatives and their impact on users and society for the better, and responsibly. In addition, we also aim to share perspectives, engage, and collaborate with others externally on technology related issues and opportunities for society.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $207000 - $300000 (USD) + 20% bonus target + equity + benefits

Learn more about benefits at Google (https://www.google.com/about/careers/applications/benefits/).

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.

- 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.