Senior Data Labeling Specialist & Annotation QA Contractor
Executed ground-truth labeling and annotation QA across over 500 high-complexity STEM and clinical-psychology tasks for AI data pipelines. Developed multi-step decision trees and rubrics for consistent label assignment and authored taxonomic schemas to enhance inter-rater reliability. Performed detailed hallucination error analysis, instruction-following evaluation, and systematic red-teaming for model alignment and safety review. • Maintained Expert Tier quality rank with 100% task acceptance rate. • Created 15+ classification schemas and scoring instruments used by annotation teams. • Collaborated with QA leads to produce error-analysis reports for rubric refinement. • Flagged misinformation and clinical harm risks during structured red-teaming protocols.