Data Analyst (AI Evaluation)

Role Title: Data Analyst (AI Evaluation)

Role Type: Contractor

Location: Remote, US-based.

micro1 is engaging 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

• Review datasets and detailed task outputs for accuracy, completeness, and adherence to guidelines.

• Identify and flag inconsistencies, errors, and outputs that deviate from provided instructions.

• Apply structured rubrics and evaluation criteria consistently across large data sets.

• Exercise sound judgment and document rationale in ambiguous scenarios where guidelines are unclear.

• Escalate patterns or recurring data issues for further analysis rather than addressing them individually.

• Provide clear, actionable written and verbal feedback pinpointing issues and suggesting resolution paths.

• Maintain meticulous attention to detail while working through high volumes of repetitive data review assignments.

Preferred Qualifications

• 3–5+ years of experience in data review, data quality, quality assurance, or analytical roles.

• Proven expertise in reviewing, validating, and critiquing data or deliverables based on explicit criteria.

• Exceptional ability to follow detailed written instructions and apply complex evaluation rubrics precisely.

• Demonstrated comfort with ambiguity, including making justifiable decisions when guidelines are incomplete.

• Advanced attention to detail and error-spotting capabilities across repetitive or high-volume work.

• Strong written and verbal communication skills for documenting findings and providing feedback.

• Baseline fluency with spreadsheets (sorting, filtering, simple formulas); SQL or hands-on data querying is a plus.

• Experience with data annotation, labeling, or rubric-based evaluation is advantageous.