Behavioral Economist Expert (Behavioral Economics)

You will create and refine material used to train AI models. Your work will connect behavioral economics research with realistic decision-making scenarios and careful review of model reasoning.

• Develop behavioral economics questions, case studies, and scenarios for AI model training.
• Test how well models apply cognitive biases, heuristics, prospect theory, loss aversion, framing effects, choice architecture, nudge design, and experimental design.
• Review model responses for accuracy, analytical quality, and alignment with behavioral economics research standards.
• Give detailed feedback and help adjust question difficulty so the material stays challenging and educational.
• Apply behavioral science knowledge to technology-related training content.

What it pays and takes

This is a remote, flexible contractor role. It is listed as part-time and requires at least 20 hours each week.

• Pay: $80 per hour.
• Schedule: 20 or more hours per week.
• Location: Open to candidates in the eligible countries listed for this role.
• Language: Professional fluency in English.
• Education: A master's or PhD in Economics, Behavioral Economics, Psychology, or a related field.
• Experience: Research in behavioral economics, experimental design, or applied behavioral science.
• Knowledge: Strong understanding of behavioral theory and empirical methods.
• Skill: Ability to assess complex reasoning and explain whether model outputs correctly apply behavioral economics principles.
• Helpful background: Academic research, policy design, corporate consulting, product design, behavioral economics publications, experimental studies, nudge design, choice architecture, A/B testing, behavioral interventions, or technology-driven behavioral applications.

How it works

Apply on OpenTrain with your resume, then complete the application on the hiring site. OpenTrain is free to join and helps you find AI training roles in one place.

About AI training work

AI training is the human work behind systems that learn from examples, including writing questions, reviewing answers, and rating model outputs. People with subject expertise are paid to help make those examples accurate, useful, and grounded in real-world knowledge.