The work
You will review business datasets and documents that support AI training. The work involves checking information carefully, finding problems, and explaining your findings clearly.
The role also includes privacy-aware document review and quality assurance. Duties and qualifications should be reviewed before applying.
- Review business datasets for accuracy, completeness, and relevance.
- Review documents while following personally identifiable information, privacy, and security procedures.
- Find, flag, and resolve data quality issues, inconsistencies, and unusual entries.
- Provide detailed written and verbal feedback and recommendations.
- Help develop and improve quality standards for data review assignments.
- Communicate findings clearly with project stakeholders.
- Maintain privacy and security compliance in project deliverables.
What it pays and takes
This is an entry-level, part-time contractor role. The work requires careful analysis, sound judgment, and consistent handling of sensitive business information.
- $31 to $60 per hour.
- Part-time contract work.
- Open to candidates across multiple countries in North and South America, Europe, Asia, Africa, and the Middle East.
- Professional fluency in English.
- Experience handling and analyzing large business datasets.
- Experience working with personally identifiable information and other sensitive material.
- Understanding of data privacy regulations and best practices.
- Background in document review, data quality assurance, audit, or related analytical work.
- Strong written and verbal communication skills.
- High attention to detail and a commitment to data accuracy and integrity.
- Ability to deliver consistent, reliable work remotely.
- Helpful experience includes privacy-focused document review, data validation, anomaly identification, or audit deliverables.
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 reviewing documents, rating information, and checking data quality. Experienced contributors are valuable because accurate, consistent reviews help improve the data used to build AI systems.