Data Analyst (AI Evaluation)

Accountabilities:: Review datasets and detailed task outputs systematically to assess accuracy, completeness, consistency, and compliance with established guidelines. Identify, document, and flag errors, inconsistencies, omissions, and outputs that do not meet specified requirements. Apply structured evaluation rubrics and quality criteria consistently across large volumes of data and review assignments. Use sound analytical judgment to evaluate ambiguous cases, interpret available guidance, and make defensible decisions when instructions are incomplete. Provide clear written and verbal feedback that precisely identifies issues, explains their impact, and recommends appropriate resolution paths. Identify recurring patterns, systemic data-quality issues, and trends that require escalation or further investigation rather than isolated correction. Maintain a high level of accuracy and consistency while completing repetitive, high-volume data review tasks. Follow detailed procedures and quality standards carefully while adapting to evolving project requirements and evaluation guidelines. Requirements: Bring 3–5+ years of experience in data analysis, data review, data quality, quality assurance, or another analytical discipline. Demonstrate proven experience reviewing, validating, auditing, or critiquing data, deliverables, or other structured outputs against defined criteria. Possess an exceptional ability to follow detailed written instructions and apply complex evaluation rubrics accurately and consistently. Demonstrate strong analytical reasoning and the ability to make well-supported decisions in situations where guidelines may be incomplete or ambiguous. Have excellent attention to detail and a proven ability to identify subtle errors, inconsistencies, and quality issues across high-volume work. Possess strong written and verbal communication skills, particularly for documenting findings, explaining rationale, and providing actionable feedback. Be highly organized and capable of maintaining consistent quality and productivity across repetitive assignments. Demonstrate strong independent judgment, reliability, and the ability to work effectively in a remote environment. Have baseline proficiency with spreadsheets, including sorting, filtering, and simple formulas. Experience with SQL, data querying, or other analytical tools is a plus. Prior experience working with artificial intelligence or machine learning is not required; strong domain expertise and analytical judgment are the primary focus. Benefits: Remote contractor opportunity with flexible participation in AI-focused data quality projects. Opportunity to apply existing data and analytical expertise to the development and evaluation of next-generation AI systems. Direct contribution to improving how AI models learn, reason, and perform through high-quality real-world data and evaluation. No prior artificial intelligence experience required. Opportunity to work on emerging AI initiatives while leveraging established professional expertise. Competitive and attractive compensation package. The contractor nature of the role provides an opportunity to contribute to project-based work remotely.