DATA ANNOTATION AND IMAGE LABELING
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ClassificationI have worked on multiple data labeling and annotation projects across different domains including NLP, document processing, compliance datasets, and structured business data enrichment. The scope of these projects typically ranged from small pilot datasets of about 5,000–10,000 data items to large-scale production datasets exceeding 500,000+ annotated records. These included text classification, named entity recognition (NER), sentiment labeling, intent tagging, image-to-text verification, and document structure annotation for AI training purposes. In terms of quality, I consistently followed strict annotation guidelines, achieving high inter-annotator agreement (typically above 92–96% depending on project complexity) through careful review, guideline refinement, and error correction cycles. I also contributed to quality assurance processes such as spot-check validation, consistency audits, and edge-case resolution to ensure dataset reliability. For performance, I maintained strong productivity benchmarks, often meeting or exceeding daily labeling targets (ranging from 1,000 to 3,000+ data points per day depending on task complexity). I also worked with iterative feedback loops to improve model training datasets, helping reduce labeling errors and improving downstream model accuracy. My experience is strengthened by my background in contract review, compliance, and legal research, which enhances my attention to detail, consistency, and ability to interpret complex labeling guidelines accurately.