Job Description:

• 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. surrogate indexes, heterogeneous-effect pipelines — to help Anthropic evaluate the downstream economic consequences of its own compute, product, and pricing decisions
• Build and maintain relationships with academic institutions, policy think tanks, and other research partners
• Work cross-functionally with other technical teams to improve our measurement infrastructure and data collection
• Translate research insights into actionable recommendations for both product decisions and policy discussions
• Amplify external engagement through research publications, policy briefs, and presentations to diverse stakeholders

Requirements:

• PhD in Economics
• Strong track record of empirical research, particularly studies combining novel data sources and economic theory or those implementing frontier methods in causal inference and machine learning
• Experience relevant to the study of AI’s impact on the economy, including:
• Labor market analysis and occupational change
• Task-based approaches to technological transformation
• Large-scale data analysis and econometric methods
• Large language models for social science research
• Policy-relevant economic research
• Experimental and quasi-experimental methods for causal inference
• Macroeconomic modeling and time series forecasting
• Agent-based modeling or large-scale simulation
• Technical skills including:
• Proficiency in Python, R, SQL, or similar tools for large-scale data analysis
• Experience working with novel datasets and measurement systems
• Comfort learning new technical tools and frameworks
• Demonstrated ability to:
• Lead complex research projects from conception to publication
• Communicate technical findings to diverse audiences
• Build relationships across academic, policy, and industry communities
• Strong interest in ensuring AI development benefits humanity
• Comfort working with AI systems and ability to think critically about their capabilities and limitations.

Benefits:

• Competitive compensation and benefits
• Optional equity donation matching
• Generous vacation and parental leave
• Flexible working hours
• Lovely office space in which to collaborate with colleagues