Data Annotation Specialist
Annotated and labeled over 50,000+ data points (text snippets, images, audio clips) with 98.7% accuracy, following detailed project-specific rubrics and improving model training datasets for NLP and vision tasks.
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I am a detail-oriented Data Labeling Specialist with over 3 years of hands-on experience in annotating and preparing high-quality datasets for AI and machine learning projects. My work spans text, image, audio, and multimodal annotation, with a proven track record of achieving 98%+ accuracy on large-scale labeling tasks. I am skilled in using tools like Labelbox, CVAT, Label Studio, SuperAnnotate, and Amazon SageMaker Ground Truth, and have contributed to domains such as NLP, computer vision, and generative AI. My expertise includes RLHF evaluation, sentiment and intent classification, toxicity detection, and red-teaming for model safety. I am fluent in English and Swahili, adapt quickly to new guidelines, and consistently deliver high-volume, high-quality results for remote and freelance projects.
Annotated and labeled over 50,000+ data points (text snippets, images, audio clips) with 98.7% accuracy, following detailed project-specific rubrics and improving model training datasets for NLP and vision tasks.
Bachelor of Science, Information Technology
IT Support / Virtual Assistant