Data Science AI Response Evaluator

You will help improve AI systems by creating realistic data science problems and reviewing the answers they produce. Your work will draw on modeling, analysis, experimentation, feature engineering, and practical data science judgment.

You will work independently on project tasks in a remote, asynchronous setting and provide clear written feedback that explains both technical issues and real-world feasibility.

• Create realistic data science problems and scenarios based on professional or research work.
• Evaluate AI-generated responses for technical accuracy and practical feasibility.
• Assess whether responses reflect real data science practice.
• Write structured feedback to help improve AI model performance.

What it pays and takes

This is an ongoing, project-based opportunity designed to fit alongside a current role. The role is part-time and requires at least 20 hours per week, with no set schedule.

Candidates should be able to explain complex data science ideas clearly, work independently, and pay close attention to detail. Experience with A/B testing, causal inference, model development, statistical analysis, or production-scale feature engineering is especially relevant.

• Pay: $120 per hour.
• Location: Open to candidates in the United States.
• Language: Strong written English communication required.
• Experience: At least one year of professional or research experience with hands-on data science work.
• Education: Bachelor's degree in Data Science, Statistics, Mathematics, Computer Science, or a related field.
• Technical background: Depth in machine learning and applied ML, statistical modeling and experimentation, or large-scale data analysis and feature engineering.
• Additional background: Experience at a recognized technology company may strengthen your fit.

How it works

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

About AI training work

AI training work is the human side of building artificial intelligence. People create examples, review model responses, and explain what good answers should look like, while experienced specialists are paid for the judgment they bring to complex topics.