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ImageI am an AI data operations and training specialist with over 3 years of hands-on experience in data annotation, quality assurance, and team leadership across diverse domains including NLP, computer vision, speech recognition, e-commerce, and clinical healthcare data. My expertise spans LLM output evaluation, prompt quality assessment, red-teaming, and RLHF data pipeline design, with a proven record of building high-performing annotation teams, developing taxonomies that improve model accuracy, and achieving >97% inter-annotator agreement through robust QA frameworks. I am highly skilled in tools like Labelbox, Scale AI, Doccano, Prodigy, SQL, Python (pandas), and Jira, and have authored comprehensive annotation guidelines and evaluation rubrics. My strong background in data governance (GDPR, DPA 2018, Caldicott), structured reasoning, and clear written communication ensures rigorous, consistent labelling even in ambiguous scenarios. I thrive on delivering actionable insights and quality data for AI model improvement, maintaining 100% client satisfaction across 75+ projects.
I annotate, classify, tag and segment these data using bounding boxes, polygon, line, and polyline tools.
Analysed, sorted and annotated pre-purchase consumer intent data for the analytics platform across different fields. Screened relevant webpages, categorising them according to predetermined criteria and instructions.
Master of Science, Marketing, Analytics and Communications
Bachelor of Laws, Law
Data Quality Analyst
QA Engineer
