Quality Assurance Lead for labeled data batches
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TextReviewed and corrected 500+ data batches produced by junior annotators, ensuring consistent label quality and high reliability. The work focused on evaluating annotations against expected outcomes and applying corrections to maintain a flawless accuracy benchmark. This role directly supported downstream AI training by improving the quality of supervised data. • Quality assurance for labeled datasets • Error identification and corrective re-labeling • Maintaining 100% accuracy rating • Supporting training pipeline readiness through clean data
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