Mubarak Isa

Key Skills

Software

Data Annotation Tech

Top Subject Matter

No subject matter listed

Top Data Types

Video

Top Label Types

Segmentation

Freelancer Overview

I am a detail-oriented professional with a strong background in research, content review, and quality asI have experience working with AI training data through structured data annotation, content evaluation, and quality control tasks across diverse domains. My work has involved labelling text datasets for classification and sentiment analysis, reviewing model-generated outputs for accuracy and bias, and ensuring adherence to detailed annotation guidelines. I am particularly strong in maintaining consistency across large datasets, applying critical thinking to ambiguous cases, and documenting edge scenarios clearly to improve model learning outcomes. What sets me apart is my analytical background and attention to methodological rigour. With advanced training in research and biostatistics, I approach data labelling systematically—prioritising precision, reliability, and reproducibility. I am comfortable working with complex technical content, following evolving guidelines, and meeting tight deadlines without compromising quality. My ability to combine domain expertise, structured reasoning, and clear communication enables me to contribute effectively to high-quality AI training data development. surance across EdTech, digital communications, and non-profit sectors. My experience includes leading teams to review and validate educational materials, conducting rigorous fact-checking, and ensuring data accuracy and consistency—skills directly relevant to data labeling and AI training data roles. I am proficient in tools such as Google Suite, Excel, PowerPoint, and Learning Management Systems like Canvas and Moodle, and have hands-on experience with compatibility testing and document verification. My ability to quickly learn new systems, combined with my commitment to accuracy and process improvement, allows me to deliver high-quality, reliable results in data-driven environments.

Labeling Experience

Audio Annotation

Data Annotation TechAudioRelationship

I worked on an audio annotation project focused on training and improving speech recognition and audio classification models. My responsibilities included listening to recorded audio files, transcribing speech accurately, labelling speaker attributes where required, identifying background noises, and tagging specific acoustic events according to detailed project guidelines. I ensured high accuracy in transcription, maintained consistency in annotation standards, and flagged unclear or low-quality recordings for review. In addition to annotation, I conducted quality assurance checks to ensure compliance with labelling protocols and data integrity requirements. This role required strong listening skills, attention to detail, linguistic precision, and the ability to work efficiently with large volumes of audio data while meeting strict accuracy benchmarks.

I worked on an audio annotation project focused on training and improving speech recognition and audio classification models. My responsibilities included listening to recorded audio files, transcribing speech accurately, labelling speaker attributes where required, identifying background noises, and tagging specific acoustic events according to detailed project guidelines. I ensured high accuracy in transcription, maintained consistency in annotation standards, and flagged unclear or low-quality recordings for review. In addition to annotation, I conducted quality assurance checks to ensure compliance with labelling protocols and data integrity requirements. This role required strong listening skills, attention to detail, linguistic precision, and the ability to work efficiently with large volumes of audio data while meeting strict accuracy benchmarks.

2025 - 2025

AI Annotation

Data Annotation TechVideoSegmentation

I worked on a video data labelling project designed to improve computer vision model performance. The role involved reviewing and annotating video footage by identifying specific objects, actions, and behavioural patterns according to detailed labelling guidelines. I carefully tagged frames, marked bounding regions where required, and ensured temporal consistency across sequences to maintain annotation accuracy. In addition to labelling, I conducted quality checks to verify alignment with project standards and flagged ambiguous cases for clarification. This contributed to improving model training accuracy, reducing false detections, and enhancing the system’s ability to recognise patterns reliably in real-world scenarios.

I worked on a video data labelling project designed to improve computer vision model performance. The role involved reviewing and annotating video footage by identifying specific objects, actions, and behavioural patterns according to detailed labelling guidelines. I carefully tagged frames, marked bounding regions where required, and ensured temporal consistency across sequences to maintain annotation accuracy. In addition to labelling, I conducted quality checks to verify alignment with project standards and flagged ambiguous cases for clarification. This contributed to improving model training accuracy, reducing false detections, and enhancing the system’s ability to recognise patterns reliably in real-world scenarios.

2025 - 2025

Education

Work History