Research Economist

DataAnnotation; United States; Remote; USD 103,000 - 172,000; Part time

Benefits offered by this job

Weekly payouts

Flexible hours

Remote work

Job summary

DataAnnotation is seeking an experienced Research Economist to help train AI models by judging the quality of frontier economic analyses and the use of data and methods. The role focuses on weighing competing analyses, spotting flawed identifications, and documenting rigorous, defendable conclusions.

The work requires clear written English, a strong economics background, and comfort with US federal data. Remote, independent contractor with flexible hours and weekly payouts.

Qualifications

• PhD in economics or Master’s preferred; undergrad in economics/finance/business considered.
• Professional experience as a research economist or economic analyst is preferred.
• Familiarity with empirical methods and econometrics.
• Comfort working with US federal economic data is a plus.
• Clear written English is essential.

Responsibilities

• Compare two competing economic analyses and assess which is more reliable for economists.
• Evaluate model output for soundness, data use, and methodology.
• Identify flawed assumptions, misread results, and gaps in reasoning.
• Write clear, structured explanations of judgments and the standards used.
• Apply domain knowledge to realistic problems drawn from applied economics research.

Skills

Written English

Economics domain knowledge

Empirical methods/econometrics

Data interpretation

Education

PhD in economics

Master’s degree in economics

Undergrad in economics/finance/business

Job description

Pay: up to $125+ /hour (depends on task difficulty and specialization)

Location: Remote · Type: Independent contractor · Hours: Flexible hours · Payouts: Weekly

Overview

We're looking for an experienced Research Economist to help train AI models. You'll judge how well frontier models reason about economic questions, check their use of data and methods, and write the analysis a working economist would stand behind.

This is a new and growing area for our company: teaching AI to reason like an economist. The models can summarize the literature. What they can't yet do reliably is weigh competing analyses of the same question, spot a flawed identification strategy, or tell an overstated conclusion from a supported one. Your judgment becomes the benchmark they are measured against.

What you'll actually do

• Compare two competing economic analyses of the same question and judge which one an economist would rely on.
• Evaluate model output for analytical soundness, correct use of data, and appropriate methodology.
• Identify flawed assumptions, misread results, overstated conclusions, and gaps in reasoning.
• Write clear, well-structured explanations of your judgments and the standards behind them.
• Apply your domain knowledge to realistic problems drawn from applied economic research.

Roles this fits

Common backgrounds: Research Economist, Economic Researcher, Postdoctoral Researcher in Economics.

What we look for

• A PhD in economics (completed or in progress) or a master’s degree in economics is preferred. Undergraduate degrees in economics, finance, or business are also considered.
• Professional experience as a research economist, economic analyst, or in a similar applied role is preferred.
• Familiarity with empirical methods, econometrics, and the interpretation of applied economic research.
• Comfort working with US federal economic data is a plus.
• Clear written English: your explanations are the training signal.
• Only applicants based in Australia, Canada, Ireland, New Zealand, the United Kingdom, or the United States will be considered for this role.

Compensation

Up to $125+/hr depending on task difficulty and specialization. Many contributors add $100k-$200k a year; some make it their full-time income.

About DataAnnotation

DataAnnotation is where 1M+ experts train the world’s leading AI models. Hundreds of millions paid to contributors to date, and the average contributor stays 5+ years. Flexible, remote, and always project-available.