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Software

Data Annotation Tech

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Freelancer Overview

I am a Computer Science student with a strong foundation in cybersecurity, data analysis, and secure software development. My experience includes building security-focused Python tools such as a password strength analyzer and a malicious URL detector, which required careful data annotation and rule-based classification to identify patterns and threats. I am skilled in Python scripting, Linux environments, and have practical knowledge of web security fundamentals, which has given me an eye for detail and accuracy—key strengths for data labeling and AI training data roles. Through my technical projects and hands-on labs, I have developed a methodical approach to analyzing and categorizing data, ensuring high-quality and reliable results. I am eager to apply my analytical mindset, problem-solving skills, and commitment to data quality in AI training and annotation tasks.

Labeling Experience

Data Annotator Contributor (Freelance)

Data Annotation TechTextEvaluation Rating

I contributed as a freelance Data Annotator at DataAnnotation.tech, focusing on evaluating AI model responses. My responsibilities included writing, refining, and reviewing prompts, as well as assessing AI-generated outputs for quality and accuracy. I provided detailed feedback and corrected errors to ensure datasets met high standards for training AI/ML models. • Applied critical thinking and meticulous attention to detail throughout annotation tasks. • Evaluated peer-contributed annotations for accuracy and guideline adherence. • Used DataAnnotation.tech proprietary tools and general productivity software. • Ensured high-quality, consistent, and clear annotated datasets.

I contributed as a freelance Data Annotator at DataAnnotation.tech, focusing on evaluating AI model responses. My responsibilities included writing, refining, and reviewing prompts, as well as assessing AI-generated outputs for quality and accuracy. I provided detailed feedback and corrected errors to ensure datasets met high standards for training AI/ML models. • Applied critical thinking and meticulous attention to detail throughout annotation tasks. • Evaluated peer-contributed annotations for accuracy and guideline adherence. • Used DataAnnotation.tech proprietary tools and general productivity software. • Ensured high-quality, consistent, and clear annotated datasets.

2025

Education

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