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.