Research Economist, Economic Research

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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
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