Data Labeling
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Data CollectionWorked on AI training and data annotation projects focused on improving machine learning model accuracy and performance. The scope of the project included labeling, categorizing, reviewing, and validating large datasets used for training AI systems in text, image, and content moderation tasks. Performed data labeling tasks such as: Image and object annotation Text classification and sentiment tagging Content moderation and data validation Bounding box and segmentation annotation Data categorization and quality review Accuracy checking and correction of labeled datasets Handled high-volume datasets while maintaining consistency and adherence to project guidelines. Collaborated with quality assurance teams to ensure annotations met accuracy standards, confidentiality requirements, and turnaround deadlines. Maintained strong quality measures including: Following strict annotation guidelines Multi-level quality checks and peer reviews Consistency verification across datasets Error correction and validation processes Meeting project accuracy benchmarks and KPIs Successfully contributed to projects involving thousands of annotated data entries with a strong focus on precision, efficiency, and data integrity.