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@veeuser5

Data annotation and quality assurance specialist evaluating AI responses for helpfulness, factual correctness, clarity, and safety.

Kenya
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What I'm looking for

I’m targeting AI evaluation and data annotation roles, and I’m eager to contribute my evaluation skills and grow across diverse AI training tasks. I’m open to flexible, project-based commitments.

In my freelance remote projects, I annotate and classify text for accuracy, sentiment, relevance, and intent, following project guidelines.

I evaluate AI-generated responses for helpfulness, factual correctness, clarity, and safety, then provide structured rankings and feedback.

I also check peer annotations for quality and consistency. Previously, as a Content Moderator & Review Analyst at Digital Content Services (Contract), I reviewed content for policy compliance and categorized misinformation, toxic language, and off-topic material.

At University Research Support Unit, I organized, cleaned, and labeled research datasets and checked survey and transcribed data. I’m targeting AI evaluation and data annotation roles, including flexible, project-based work.

Experience

Work history, roles, and key accomplishments

FP
Current

Data Annotation & Quality Assurance Specialist

Freelance / Remote Projects

Jan 2024 - Present (2 years 9 months)

Annotated and classified large volumes of text data for accuracy, sentiment, relevance, and intent per project guidelines. Evaluated AI-generated responses for helpfulness, factual correctness, clarity, and safety, providing structured rankings and feedback.

Education

Degrees, certifications, and relevant coursework

University of Nairobi logoUN

University of Nairobi

Bachelor of Science, Information Technology

2016 - 2020

Bachelor of Science in Information Technology with coursework in data structures, database systems, human-computer interaction, research methods, and technical writing.

OP

Online Professional Development Program

Certificate, Data Annotation and AI Fundamentals

Certificate in Data Annotation and AI Fundamentals covering data labeling best practices, evaluation frameworks for language models, and quality control for AI training data.

Tech stack

Software and tools used professionally

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