Statistician & Experimentation Expert

You will create and evaluate AI training data focused on causal inference and A/B testing. Your work will test whether AI models can reason correctly about study design, statistical analysis, and results.

• Write realistic statistics scenarios covering study design, analysis, and interpretation.
• Create reference answers that explain the reasoning behind modeling choices.
• Review AI model outputs for sound methods and correct interpretation.
• Identify analyses that look rigorous but lead to invalid conclusions.
• Apply expert judgment in experimental design, causal inference, and A/B testing.

What it pays and takes

This is an ongoing, independent contractor role. The work is fully remote and asynchronous, with flexibility to change your schedule from week to week.

• Pay: $80 to $150 per hour, paid weekly through Stripe.
• Hours: At least 10 hours per week, with no maximum.
• Location: Remote, open to candidates in the countries specified for this role.
• Language: Fluent English.
• Education: An advanced degree in statistics, biostatistics, or a closely related quantitative field.
• Experience: At least two years of applied statistics or experimentation experience.
• Skills: Strong knowledge of experimental design, causal inference, or A/B testing.
• Technical skills: Fluency in R or Python for statistical analysis.
• Communication: Ability to document and explain modeling decisions clearly.
• Helpful background: Large-scale experimentation platforms, Bayesian methods, instrumental variables, synthetic control methods, or a PhD in statistics, biostatistics, or econometrics.

How it works

Apply on OpenTrain with your resume, then complete the application on the hiring site.

About AI training work

AI training is the human work behind systems that learn from examples, including writing and rating model responses and checking whether outputs are accurate and well reasoned. People with advanced statistical expertise are paid to create difficult examples and judge model quality in specialized areas.