Ihechukwu Iroha

I’m a Data Annotator experienced in delivering high-volume, high-accuracy labeled datasets for AI training. I focus on data quality, consistency, and reliability—because strong labels directly improve model performance.

At Atlas Capture (Label), I processed and annotated 10,000+ image and text data points, sustaining 98%+ annotation accuracy while meeting tight deadlines. I delivered 300–500 annotations daily in high-volume workflows with 100% deadline compliance, and I actively identified and corrected labeling errors during review cycles to reduce rework.

Previously, with Hive Microtasks, I completed and validated 5,000+ data entries, ensuring high data integrity across multiple task types. I consistently met performance benchmarks, earned top-quality ratings for accuracy and compliance, and reduced error rates through thorough dataset reviews before submission.

I’m committed to accuracy and compliance in every workflow, using structured validation and clean formatting to keep datasets usable at scale. I enjoy work that blends speed with precision—where attention to detail and clear labeling standards matter.