[Hiring] Member of Technical Staff, Economics Research @24-MAG

Role Description

We are sharing a full-time research opportunity for an experienced economist with deep expertise in applied economic research, econometrics, causal inference, empirical analysis, benchmarking, and AI evaluation to help develop rigorous frameworks for measuring and advancing AI performance across complex economic reasoning and research workflows. The role sits at the intersection of economics, large language models, agentic systems, and applied AI research. The successful candidate will:

• Design economics-focused evaluation frameworks
• Develop datasets and benchmarks
• Investigate model behaviour and failure modes
• Collaborate across research, engineering, product, and policy-oriented teams

Qualifications

• PhD in Economics or a closely related quantitative discipline
• 3–5 years of post-PhD experience in applied economic research across industry, academia, public policy, or a comparable environment
• Strong expertise in econometrics, applied microeconomics, causal inference, and empirical research
• Strong quantitative and statistical-analysis skills
• Experience working with large datasets
• Proficiency with SQL and Python, R, or comparable statistical programming tools
• Expertise in one or more areas such as labour economics, macroeconomics, industrial organisation, productivity, technological change, or AI economics
• Demonstrated ability to develop analytical methodologies
• Strong critical-thinking and problem-solving skills
• Excellent written and verbal communication
• Experience producing research papers, technical reports, or policy briefs
• Experience collaborating across research, engineering, product, or policy teams
• Experience developing AI evaluations, benchmarks, agentic economic workflows, or enterprise AI applications is advantageous

Requirements

• Conduct applied economic research and translate findings into measurable AI-evaluation criteria
• Design benchmarks, scoring methodologies, and quality rubrics for economic reasoning and research tasks
• Develop datasets and test cases involving empirical analysis, causal reasoning, policy questions, and real-world economic workflows
• Evaluate model-generated economic analysis for methodological validity, robustness, and research quality
• Refine evaluation frameworks as model capabilities evolve
• Apply econometric and causal-inference methods to AI evaluation problems
• Review assumptions, identification strategies, statistical validity, and robustness
• Analyse model behaviour, recurring failure modes, and reasoning weaknesses
• Evaluate applied microeconomic and quantitative reasoning
• Translate observed model failures into new research questions or benchmark tasks
• Evaluate AI systems performing multi-step economic research, analysis, and synthesis
• Assess reliability across agentic workflows and complex reasoning chains
• Work with large datasets using SQL and Python, R, or comparable statistical tools
• Investigate enterprise and institutional use cases for AI-assisted economic research
• Validate analytical outputs for methodological and computational accuracy
• Produce technical reports, research findings, benchmark documentation, and policy-oriented materials
• Communicate complex economic concepts clearly to interdisciplinary audiences
• Collaborate with economists, researchers, engineers, product teams, and policy-oriented stakeholders
• Translate economic research requirements into technical evaluation frameworks
• Support cross-functional projects connecting economics, AI research, and product development

Benefits

• Full-time engagement
• Fully remote
• Compensation: $400,000–$800,000/year

Engagement Details

• Work will involve economic research, econometrics, causal inference, empirical analysis, benchmark development, dataset creation, model evaluation, and agentic economic workflows
• PhD-level economics expertise and substantial applied research experience are central to this role
• Responsibilities may span labour economics, macroeconomics, industrial organisation, productivity, technological change, AI economics, enterprise AI, and related research areas
• Regular collaboration with economists, researchers, engineers, product teams, and other interdisciplinary stakeholders is expected
• Project scope, research priorities, benchmark methodologies, and evaluation frameworks may evolve as AI capabilities and research requirements develop
• Work must be completed without using confidential, proprietary, unpublished, embargoed, restricted-access, or otherwise protected information belonging to any employer, research institution, government organisation, client, data provider, collaborator, or other third party