Mendota
The Mendota project at Appen is typically a data labeling and annotation project.
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I am a detail-oriented data rater and quality analyst with hands-on experience in data annotation, relevance assessment, and structured data validation for AI training projects. My background includes evaluating model outputs for accuracy, performing usability and functional testing for web and mobile applications, and contributing to quality review tasks across multiple domains. With a Bachelor’s degree in Computer Science and a solid understanding of artificial intelligence concepts, I am skilled in analytical thinking, bug reporting, and quality assurance testing. I am proficient in using Microsoft Office, Google Workspace, and basic Python, and I am committed to delivering high-quality, consistent results in remote environments.
The Mendota project at Appen is typically a data labeling and annotation project.
The purpose of the project is to collect audios to improve speech recognition
The Kanyaru project involved evaluating and improving AI-generated content to enhance model accuracy, relevance, and safety. Responsibilities included reviewing model responses, rating output quality based on detailed guidelines, and identifying factual errors, bias, or policy violations. The role required strong analytical skills, attention to detail, and consistent adherence to project standards.
Bachelor of Science, Computer Science
QA Tester ,AI Trainer / Rater,Linguistic & Language Data Projects