Mugo Harmaton

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Freelancer Overview

I am an experienced Medical Laboratory Technologist and Medical Writer with a strong background in data annotation and AI training data, particularly within the medical domain. My expertise includes annotating and evaluating both medical and general datasets to support the development of AI models, ensuring accuracy, safety, and adherence to clinical guidelines. I have worked with platforms such as Outlier, Mindrift, and Clickworker, consistently producing high-quality, QA-compliant data. My attention to detail, analytical mindset, and proficiency with tools like Microsoft Office and Google Workspace enable me to efficiently manage large volumes of data and documentation. I am passionate about leveraging my clinical and technical skills to contribute to innovative AI and data-driven projects.

Labeling Experience

Freelance Data Annotation & Content Evaluation Specialist

LabelboxImageEvaluation Rating

Performed structured text analysis, labeling, and evaluation to support AI model development and refinement. Consistently adhered to complex project guidelines to ensure annotated data met quality standards for downstream machine learning workflows. Conducted quality reviews of written outputs to verify clarity, factual accuracy, and logical consistency. • Labeled data critical for machine learning model training and improvements. • Followed detailed, guideline-driven annotation and review processes for text datasets. • Supported model evaluation tasks by providing high-quality, structured data. • Maintained high accuracy and attention to detail in ongoing evaluation work.

Performed structured text analysis, labeling, and evaluation to support AI model development and refinement. Consistently adhered to complex project guidelines to ensure annotated data met quality standards for downstream machine learning workflows. Conducted quality reviews of written outputs to verify clarity, factual accuracy, and logical consistency. • Labeled data critical for machine learning model training and improvements. • Followed detailed, guideline-driven annotation and review processes for text datasets. • Supported model evaluation tasks by providing high-quality, structured data. • Maintained high accuracy and attention to detail in ongoing evaluation work.

2020

Education

Work History