Business Data AI Evaluation Analyst

You will help evaluate how well AI handles real-world business analysis tasks. The work combines data privacy review, analytical quality checks, and clear scoring standards for AI performance.

• Review business datasets for personally identifiable information and flag sensitive content using appropriate privacy practices.
• Assess AI responses to business and data analysis scenarios, then provide feedback to improve accuracy and precision.
• Validate, reconcile, and verify analytical outputs using quality assurance methods.
• Create objective evaluation frameworks and grading criteria for analytical AI performance.
• Find inconsistencies and help improve datasets, evaluation guidance, and best practices for AI-driven analytics.

What it pays and takes

This is a part-time contractor role performed remotely from the United States. The role is marked entry level, and the requirements below describe the experience and education needed for the work.

• Pay: $50 to $80 per hour.
• Location: Open to candidates in the United States.
• Schedule: Part-time.
• Language: Fluency in English.
• Experience: At least three years as a business analyst or data analyst working with large business datasets.
• Skills: Data validation, reconciliation, quality assurance, analytical-output verification, and handling personally identifiable information or other sensitive material.
• Education: A bachelor's degree in business, analytics, statistics, information systems, or a related field, or equivalent professional experience.
• Helpful: SQL, Excel, business intelligence or data visualization tools, an advanced degree or professional certification in business analysis or data analytics, or experience with AI, data labeling, or model evaluation.

How it works

Apply on OpenTrain with your resume, then complete the application on the hiring site. OpenTrain is where you find this role and start your application; it does not employ or pay you for the work.

About AI training work

AI training is the human work behind systems that learn from examples, including reviewing model answers and checking data quality. People with business and analytical experience help set reliable standards for judging whether AI outputs are accurate and useful.