Data Labeling Specialist
Worked as a Data Labeling Specialist on AI training projects involving document annotation, question answering datasets, and education and academic content classification. Tasks included reviewing and labeling structured and unstructured text data, tagging questions with appropriate answers, and ensuring accurate categorization of academic materials such as essays, research excerpts, and learning content. Responsibilities included maintaining high-quality annotations by strictly following project guidelines, resolving ambiguous cases through careful contextual analysis, and ensuring consistency across large datasets. The project involved approximately 10,000+ data items, requiring attention to detail and efficient workflow management under tight deadlines. Quality measures adhered to included ≥95% accuracy rate, double-check verification for sensitive labels, adherence to annotation guidelines, and periodic quality audits by supervisors to ensure data reliability for machine learning model training.