PII Document Review Analyst
The work
As a PII Document Review Analyst, you will review transformed datasets and documents used in an AI training workflow focused on protecting sensitive information. You will judge whether personally identifiable information has been properly redacted or anonymized and whether privacy standards are applied consistently.
You will record discrepancies using the provided tools and templates, then give clear feedback when sensitive details are missed or mishandled. The work calls for careful review, sound privacy judgment, and accurate written explanations.
- Conduct spot checks of transformed datasets for accurate PII redaction or anonymization.
- Review documents in detail to assess whether data privacy standards are being followed.
- Identify sensitive information that was missed or improperly handled.
- Document observations and discrepancies with provided tools and templates.
- Provide clear, actionable feedback that supports consistent data quality and privacy handling.
What it pays and takes
This opportunity is listed as entry level, but it requires a strong understanding of personally identifiable information and data privacy regulations. Relevant experience reviewing legal or other sensitive documents may come from contract review, quality assurance, eDiscovery, compliance, litigation support, managed legal services, privacy analysis, or information governance.
- Pay: $31 to $60 per hour.
- Time commitment: 20 or more hours per week.
- Work arrangement: Remote contractor opportunity.
- Location: Available to professionals in the eligible countries listed for this role.
- Language: English.
- You need strong analytical judgment, close attention to detail, and consistent accuracy.
- You must be able to explain privacy discrepancies clearly and constructively.
- You must handle sensitive data with discretion and care.
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 and evaluating model data. Careful privacy review helps make those examples safer and more reliable, and people with relevant legal, compliance, or privacy experience can contribute specialized judgment.
