AI QA Analyst

2+ years of experience in QA, data analytics, or testing roles focused on data quality or AI output validation. Strong ability to analyze and interpret large datasets or AI-generated outputs to identify issues and trends. Proficiency in SQL for investigating data integrity, creating reports, and validating data logic. Hands-on experience with manual and exploratory testing in data-centric or model-driven environments. Excellent analytical, classification, and documentation skills—able to articulate nuanced findings beyond pass/fail results. Highly organized with the ability to manage multiple concurrent QA cycles. Naturally curious and detail-oriented, with the confidence to question assumptions and surface data-driven insights.

Conduct QA and validation on outputs from AI models, data pipelines, and automation workflows. Identify and document errors, inconsistencies, and anomalies in largescale datasets or model-generated outputs. Collaborate with data scientists, engineers, and product teams to triage and investigate data quality issues. Develop and maintain SQL-based validation scripts and reports to track data integrity across systems. Perform manual and exploratory testing of AI features, classification systems, and automation logic. Create clear documentation and QA reports that describe findings, reproducible test cases, and impact assessments. Support continuous improvement of model evaluation frameworks and QA processes. Participate in cross-functional reviews to ensure AI systems meet quality and performance standards prior to deployment.