Research Economist, Economic Research at Anthropic
- Pay: $320,000–$405,000 a year.
- Remote.
About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the Role As an Economist at Anthropic, you will work to measure and understand AI's effects on the global economy. You will make fundamental contributions to the development of the Anthropic Economic Index, establishing new methodologies to measure the usage, diffusion, and impact of AI throughout the economy using privacy-preserving tools and novel data sources. You will use frontier methods in econometrics, machine learning, and structural estimation. Such rigour will drive impact, shaping both policy discussions externally and informing Anthropic’s internal business and product decisions. Our team combines rigorous empirical methods with novel measurement approaches. We're building first-of-its-kind datasets tracking AI's impact on labor markets, productivity, and economic transformation. Using our privacy-preserving measurement system (Clio), we analyze millions of real-world AI interactions to understand how AI augments and automates work across different occupations and tasks. Responsibilities Make fundamental contributions to the development and expansion of the Anthropic Economic Index, including quarterly reports and industry-specific deep dives Design and conduct empirical research on AI's economic effects, drawing on external data sources and the privacy-preserving measurement systems internally Develop new methodological approaches for studying AI's impact on: Labor markets and the future of work Productivity and task transformation Economic inequality and displacement Industry-specific disruption and adaptation Aggregate economic trajectories (GDP, productivity, unemployment) under varying AI-adoption scenarios Develop causal-inference tooling — e.g. …