Analyste de données (Évaluation d'IA)

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.