Data labelling intern
Used bounding boxes and polygons to annotate objects in high-resolution frames. • Participated in weekly feedback sessions to refine annotation accuracy. Contributed to internal knowledge base for labeling best practices
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I am a dedicated data annotation and labeling specialist with hands-on experience supporting AI and machine learning projects across diverse domains, including computer vision, natural language processing, and healthcare. I have annotated and quality-checked over 10,000 images using tools like Labelbox and CVAT, performed text classification and named entity recognition for NLP datasets, and contributed to the development of AI-powered diagnostic systems by labeling medical images and clinical data in compliance with healthcare standards. My background includes ensuring data integrity and consistency, collaborating with cross-functional teams, and leveraging industry-standard tools such as SuperAnnotate and Prodigy. I am adept at following detailed guidelines, conducting rigorous quality assurance, and continuously improving labeling processes to create high-quality, bias-reduced training datasets that enhance model performance and reliability.
Used bounding boxes and polygons to annotate objects in high-resolution frames. • Participated in weekly feedback sessions to refine annotation accuracy. Contributed to internal knowledge base for labeling best practices
Bachelor of Science, Project Management
Certificate, Data Labelling
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