About the ProjectPareto.AI is a human data-collection platform connecting leading AI researchers with trusted industry experts to collaborate on AI alignment, safety, and training projects. We are partnering with a frontier AI lab to evaluate an AI model's ability to replicate empirical economics research findings.
What You'll DoIdentify suitable causal economics papers with publicly available replication dataWrite prompts asking the AI model to replicate findings given a research question, dataset, codebook, and contextWrite rubrics to evaluate the AI model's performance across each step of the empirical pipeline:Data cleaningVariable constructionSpecification choiceRobustness judgment
Who We're Looking ForPhD in Economics (required)Hands-on experience with causal inference methods — DiD, IV, RDD, RCT, natural experimentsFamiliarity with replication-friendly microdata — NLSY, ACS, CPS, administrative dataProficient in STATA, R, or PythonStrong understanding of empirical research workflow from raw data to published resultsBonus: experience with AI/ML tools or interest in AI evaluation
Ideal BackgroundActive or former academic economist at a research universityPublished or working papers in applied microeconomicsFields: labor, health, development, public, environmental economics
Why JoinContribute to cutting-edge AI safety and alignment researchFlexible part-time remote work — task-based engagementCollaborate with a global network of economists and AI researchersCompetitive compensation per completed taskCompensation - $100/hr USD
Apply:To apply, submit your CV. We review every application personally — no automated screening. If your background is a strong fit, you'll receive a direct link to join the project and complete your application.
About the ProjectPareto.AI is a human data-collection platform connecting leading AI researchers with trusted industry experts to collaborate on AI alignment, safety, and training projects. We are partnering with a frontier AI lab to evaluate an AI model's ability to replicate empirical economics research findings.
What You'll DoIdentify suitable causal economics papers with publicly available replication dataWrite prompts asking the AI model to replicate findings given a research question, dataset, codebook, and contextWrite rubrics to evaluate the AI model's performance across each step of the empirical pipeline:Data cleaningVariable constructionSpecification choiceRobustness judgment
Who We're Looking ForPhD in Economics (required)Hands-on experience with causal inference methods — DiD, IV, RDD, RCT, natural experimentsFamiliarity with replication-friendly microdata — NLSY, ACS, CPS, administrative dataProficient in STATA, R, or PythonStrong understanding of empirical research workflow from raw data to published resultsBonus: experience with AI/ML tools or interest in AI evaluation
Ideal BackgroundActive or former academic economist at a research universityPublished or working papers in applied microeconomicsFields: labor, health, development, public, environmental economics
Why JoinContribute to cutting-edge AI safety and alignment researchFlexible part-time remote work — task-based engagementCollaborate with a global network of economists and AI researchersCompetitive compensation per completed taskCompensation - $100/hr USD
Apply:To apply, submit your CV. We review every application personally — no automated screening. If your background is a strong fit, you'll receive a direct link to join the project and complete your application.
About the ProjectPareto.AI is a human data-collection platform connecting leading AI researchers with trusted industry experts to collaborate on AI alignment, safety, and training projects. We are partnering with a frontier AI lab to evaluate an AI model's ability to replicate empirical economics research findings.
What You'll DoIdentify suitable causal economics papers with publicly available replication dataWrite prompts asking the AI model to replicate findings given a research question, dataset, codebook, and contextWrite rubrics to evaluate the AI model's performance across each step of the empirical pipeline:Data cleaningVariable constructionSpecification choiceRobustness judgment
Who We're Looking ForPhD in Economics (required)Hands-on experience with causal inference methods — DiD, IV, RDD, RCT, natural experimentsFamiliarity with replication-friendly microdata — NLSY, ACS, CPS, administrative dataProficient in STATA, R, or PythonStrong understanding of empirical research workflow from raw data to published resultsBonus: experience with AI/ML tools or interest in AI evaluation
Ideal BackgroundActive or former academic economist at a research universityPublished or working papers in applied microeconomicsFields: labor, health, development, public, environmental economics
Why JoinContribute to cutting-edge AI safety and alignment researchFlexible part-time remote work — task-based engagementCollaborate with a global network of economists and AI researchersCompetitive compensation per completed taskCompensation - $100/hr USD
Apply:To apply, submit your CV. We review every application personally — no automated screening. If your background is a strong fit, you'll receive a direct link to join the project and complete your application.
About the ProjectPareto.AI is a human data-collection platform connecting leading AI researchers with trusted industry experts to collaborate on AI alignment, safety, and training projects. We are partnering with a frontier AI lab to evaluate an AI model's ability to replicate empirical economics research findings.
What You'll DoIdentify suitable causal economics papers with publicly available replication dataWrite prompts asking the AI model to replicate findings given a research question, dataset, codebook, and contextWrite rubrics to evaluate the AI model's performance across each step of the empirical pipeline:Data cleaningVariable constructionSpecification choiceRobustness judgment
Who We're Looking ForPhD in Economics (required)Hands-on experience with causal inference methods — DiD, IV, RDD, RCT, natural experimentsFamiliarity with replication-friendly microdata — NLSY, ACS, CPS, administrative dataProficient in STATA, R, or PythonStrong understanding of empirical research workflow from raw data to published resultsBonus: experience with AI/ML tools or interest in AI evaluation
Ideal BackgroundActive or former academic economist at a research universityPublished or working papers in applied microeconomicsFields: labor, health, development, public, environmental economics
Why JoinContribute to cutting-edge AI safety and alignment researchFlexible part-time remote work — task-based engagementCollaborate with a global network of economists and AI researchersCompetitive compensation per completed taskCompensation - $100/hr USD
Apply:To apply, submit your CV. We review every application personally — no automated screening. If your background is a strong fit, you'll receive a direct link to join the project and complete your application.
About the ProjectPareto.AI is a human data-collection platform connecting leading AI researchers with trusted industry experts to collaborate on AI alignment, safety, and training projects. We are partnering with a frontier AI lab to evaluate an AI model's ability to replicate empirical economics research findings.
What You'll DoIdentify suitable causal economics papers with publicly available replication dataWrite prompts asking the AI model to replicate findings given a research question, dataset, codebook, and contextWrite rubrics to evaluate the AI model's performance across each step of the empirical pipeline:Data cleaningVariable constructionSpecification choiceRobustness judgment
Who We're Looking ForPhD in Economics (required)Hands-on experience with causal inference methods — DiD, IV, RDD, RCT, natural experimentsFamiliarity with replication-friendly microdata — NLSY, ACS, CPS, administrative dataProficient in STATA, R, or PythonStrong understanding of empirical research workflow from raw data to published resultsBonus: experience with AI/ML tools or interest in AI evaluation
Ideal BackgroundActive or former academic economist at a research universityPublished or working papers in applied microeconomicsFields: labor, health, development, public, environmental economics
Why JoinContribute to cutting-edge AI safety and alignment researchFlexible part-time remote work — task-based engagementCollaborate with a global network of economists and AI researchersCompetitive compensation per completed taskCompensation - $100/hr USD
Apply:To apply, submit your CV. We review every application personally — no automated screening. If your background is a strong fit, you'll receive a direct link to join the project and complete your application.
About the ProjectPareto.AI is a human data-collection platform connecting leading AI researchers with trusted industry experts to collaborate on AI alignment, safety, and training projects. We are partnering with a frontier AI lab to evaluate an AI model's ability to replicate empirical economics research findings.
What You'll DoIdentify suitable causal economics papers with publicly available replication dataWrite prompts asking the AI model to replicate findings given a research question, dataset, codebook, and contextWrite rubrics to evaluate the AI model's performance across each step of the empirical pipeline:Data cleaningVariable constructionSpecification choiceRobustness judgment
Who We're Looking ForPhD in Economics (required)Hands-on experience with causal inference methods — DiD, IV, RDD, RCT, natural experimentsFamiliarity with replication-friendly microdata — NLSY, ACS, CPS, administrative dataProficient in STATA, R, or PythonStrong understanding of empirical research workflow from raw data to published resultsBonus: experience with AI/ML tools or interest in AI evaluation
Ideal BackgroundActive or former academic economist at a research universityPublished or working papers in applied microeconomicsFields: labor, health, development, public, environmental economics
Why JoinContribute to cutting-edge AI safety and alignment researchFlexible part-time remote work — task-based engagementCollaborate with a global network of economists and AI researchersCompetitive compensation per completed taskCompensation - $100/hr USD
Apply:To apply, submit your CV. We review every application personally — no automated screening. If your background is a strong fit, you'll receive a direct link to join the project and complete your application.
About the ProjectPareto.AI is a human data-collection platform connecting leading AI researchers with trusted industry experts to collaborate on AI alignment, safety, and training projects. We are partnering with a frontier AI lab to evaluate an AI model's ability to replicate empirical economics research findings.
What You'll DoIdentify suitable causal economics papers with publicly available replication dataWrite prompts asking the AI model to replicate findings given a research question, dataset, codebook, and contextWrite rubrics to evaluate the AI model's performance across each step of the empirical pipeline:Data cleaningVariable constructionSpecification choiceRobustness judgment
Who We're Looking ForPhD in Economics (required)Hands-on experience with causal inference methods — DiD, IV, RDD, RCT, natural experimentsFamiliarity with replication-friendly microdata — NLSY, ACS, CPS, administrative dataProficient in STATA, R, or PythonStrong understanding of empirical research workflow from raw data to published resultsBonus: experience with AI/ML tools or interest in AI evaluation
Ideal BackgroundActive or former academic economist at a research universityPublished or working papers in applied microeconomicsFields: labor, health, development, public, environmental economics
Why JoinContribute to cutting-edge AI safety and alignment researchFlexible part-time remote work — task-based engagementCollaborate with a global network of economists and AI researchersCompetitive compensation per completed taskCompensation - $100/hr USD
Apply:To apply, submit your CV. We review every application personally — no automated screening. If your background is a strong fit, you'll receive a direct link to join the project and complete your application.
About the ProjectPareto.AI is a human data-collection platform connecting leading AI researchers with trusted industry experts to collaborate on AI alignment, safety, and training projects. We are partnering with a frontier AI lab to evaluate an AI model's ability to replicate empirical economics research findings.
What You'll DoIdentify suitable causal economics papers with publicly available replication dataWrite prompts asking the AI model to replicate findings given a research question, dataset, codebook, and contextWrite rubrics to evaluate the AI model's performance across each step of the empirical pipeline:Data cleaningVariable constructionSpecification choiceRobustness judgment
Who We're Looking ForPhD in Economics (required)Hands-on experience with causal inference methods — DiD, IV, RDD, RCT, natural experimentsFamiliarity with replication-friendly microdata — NLSY, ACS, CPS, administrative dataProficient in STATA, R, or PythonStrong understanding of empirical research workflow from raw data to published resultsBonus: experience with AI/ML tools or interest in AI evaluation
Ideal BackgroundActive or former academic economist at a research universityPublished or working papers in applied microeconomicsFields: labor, health, development, public, environmental economics
Why JoinContribute to cutting-edge AI safety and alignment researchFlexible part-time remote work — task-based engagementCollaborate with a global network of economists and AI researchersCompetitive compensation per completed taskCompensation - $100/hr USD
Apply:To apply, submit your CV. We review every application personally — no automated screening. If your background is a strong fit, you'll receive a direct link to join the project and complete your application.
About the ProjectPareto.AI is a human data-collection platform connecting leading AI researchers with trusted industry experts to collaborate on AI alignment, safety, and training projects. We are partnering with a frontier AI lab to evaluate an AI model's ability to replicate empirical economics research findings.
What You'll DoIdentify suitable causal economics papers with publicly available replication dataWrite prompts asking the AI model to replicate findings given a research question, dataset, codebook, and contextWrite rubrics to evaluate the AI model's performance across each step of the empirical pipeline:Data cleaningVariable constructionSpecification choiceRobustness judgment
Who We're Looking ForPhD in Economics (required)Hands-on experience with causal inference methods — DiD, IV, RDD, RCT, natural experimentsFamiliarity with replication-friendly microdata — NLSY, ACS, CPS, administrative dataProficient in STATA, R, or PythonStrong understanding of empirical research workflow from raw data to published resultsBonus: experience with AI/ML tools or interest in AI evaluation
Ideal BackgroundActive or former academic economist at a research universityPublished or working papers in applied microeconomicsFields: labor, health, development, public, environmental economics
Why JoinContribute to cutting-edge AI safety and alignment researchFlexible part-time remote work — task-based engagementCollaborate with a global network of economists and AI researchersCompetitive compensation per completed taskCompensation - $100/hr USD
Apply:To apply, submit your CV. We review every application personally — no automated screening. If your background is a strong fit, you'll receive a direct link to join the project and complete your application.
About the ProjectPareto.AI is a human data-collection platform connecting leading AI researchers with trusted industry experts to collaborate on AI alignment, safety, and training projects. We are partnering with a frontier AI lab to evaluate an AI model's ability to replicate empirical economics research findings.
What You'll DoIdentify suitable causal economics papers with publicly available replication dataWrite prompts asking the AI model to replicate findings given a research question, dataset, codebook, and contextWrite rubrics to evaluate the AI model's performance across each step of the empirical pipeline:Data cleaningVariable constructionSpecification choiceRobustness judgment
Who We're Looking ForPhD in Economics (required)Hands-on experience with causal inference methods — DiD, IV, RDD, RCT, natural experimentsFamiliarity with replication-friendly microdata — NLSY, ACS, CPS, administrative dataProficient in STATA, R, or PythonStrong understanding of empirical research workflow from raw data to published resultsBonus: experience with AI/ML tools or interest in AI evaluation
Ideal BackgroundActive or former academic economist at a research universityPublished or working papers in applied microeconomicsFields: labor, health, development, public, environmental economics
Why JoinContribute to cutting-edge AI safety and alignment researchFlexible part-time remote work — task-based engagementCollaborate with a global network of economists and AI researchersCompetitive compensation per completed taskCompensation - $100/hr USD
Apply:To apply, submit your CV. We review every application personally — no automated screening. If your background is a strong fit, you'll receive a direct link to join the project and complete your application.
