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.