Senior Data Analyst, Business AI Evaluation

You will review business datasets and AI responses to determine whether the data handling and analysis are accurate, consistent, and useful. You will also help define clear standards for judging AI performance on real-world business analysis tasks.

• Review business datasets for personally identifiable information and other sensitive content.
• Flag sensitive material using appropriate data privacy practices.
• Assess AI responses to business and data analysis scenarios.
• Provide feedback that improves the accuracy and precision of AI results.
• Validate, reconcile, and verify analytical outputs as part of quality assurance work.
• Create objective evaluation frameworks and grading criteria for analytical AI performance.
• Identify inconsistencies and help improve datasets, evaluation guidelines, and best practices.

This is a part-time contractor role performed remotely from the United States. The listing is marked entry level, and the role also requires substantial experience working with business data.

• Pay: $50 to $60 per hour.
• Work arrangement: Part-time and remote.
• Location: Open to candidates in the United States.
• Language: Fluent in English.
• Experience: At least three years as a Business Analyst or Data Analyst working with large business datasets.
• Hands-on experience with data validation, reconciliation, quality assurance, and verification of analytical outputs.
• Experience managing personally identifiable information and other sensitive material.
• A bachelor's degree in business, analytics, statistics, information systems, or a related field, or equivalent professional experience.
• Helpful skills: SQL, Excel, and business intelligence or data visualization tools.
• Also valued: An advanced degree or professional certification in business analysis or data analytics, or experience with AI, data labeling, or model evaluation.

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

AI training is the human work behind artificial intelligence, including reviewing data and judging whether model responses are accurate and useful. People with business, analytics, and privacy experience help set reliable standards for how AI systems perform on real tasks.