Data Quality Analyst (Remote)

• Role Title: Data Analyst (AI Evaluation)
• Role Type: Contractor
• Location: Remote, US-based.

Data Analysts to contribute their knowledge and expertise to a customer's data quality project. In this role, you'll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input. No prior experience in AI is required — your domain knowledge is what matters.

Scope of Work

• 1. Review datasets and detailed task outputs for accuracy, completeness, and adherence to guidelines.
• 2. Identify and flag inconsistencies, errors, and outputs that deviate from provided instructions.
• 3. Apply structured rubrics and evaluation criteria consistently across large data sets.
• 4. Exercise sound judgment and document rationale in ambiguous scenarios where guidelines are unclear.
• 5. Escalate patterns or recurring data issues for further analysis rather than addressing them individually.
• 6. Provide clear, actionable written and verbal feedback pinpointing issues and suggesting resolution paths.
• 7. Maintain meticulous attention to detail while working through high volumes of repetitive data review assignments.

Preferred Qualifications

• 1. 3–5+ years of experience in data review, data quality, quality assurance, or analytical roles.
• 2. Proven expertise in reviewing, validating, and critiquing data or deliverables based on explicit criteria.
• 3. Exceptional ability to follow detailed written instructions and apply complex evaluation rubrics precisely.
• 4. Demonstrated comfort with ambiguity, including making justifiable decisions when guidelines are incomplete.
• 5. Advanced attention to detail and error-spotting capabilities across repetitive or high-volume work.
• 6. Strong written and verbal communication skills for documenting findings and providing feedback.
• 7. Baseline fluency with spreadsheets (sorting, filtering, simple formulas); SQL or hands-on data querying is a plus.
• 8. Experience with data annotation, labeling, or rubric-based evaluation is advantageous.

What You'll Do

• 1. Review datasets and detailed task outputs for accuracy, completeness, and adherence to guidelines.
• 2. Identify and flag inconsistencies, errors, and outputs that deviate from provided instructions.
• 3. Apply structured rubrics and evaluation criteria consistently across large data sets.
• 4. Exercise sound judgment and document rationale in ambiguous scenarios where guidelines are unclear.
• 5. Escalate patterns or recurring data issues for further analysis rather than addressing them individually.
• 6. Provide clear, actionable written and verbal feedback pinpointing issues and suggesting resolution paths.

Apply Now!