AI Data Annotation Specialist
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I am an AI Data Annotation and Quality Assurance specialist with a strong academic background in Statistics and Programming and hands-on experience supporting the training and evaluation of large-scale machine learning models. I have worked with global AI vendors including REVELO, RWS, Lionbridge/TELUS International, Clickworker, OneForma, and Atlas Capture, delivering high-precision annotations across text, search relevance, audio, video, and multimodal datasets. My expertise spans search intent classification, product–query relevance evaluation, taxonomy-based labeling, and quality auditing, with a strong focus on accuracy, consistency, and guideline compliance.
Proficient in Python, SQL, and statistical tools, I combine analytical rigor with production-level annotation workflows to improve model performance and data reliability in real-world AI systems.
AI data specialist with multi-year experience in large-scale data annotation, search relevance evaluation, and quality assurance for machine learning systems. Proven track record working with global AI vendors (REVELO, RWS, TELUS International/Lionbridge, Clickworker, OneForma, Atlas Capture) on projects involving text, audio, video, and multimodal datasets. Core strengths include query-to-product relevance assessment, intent classification, taxonomy-based labeling, annotation validation, and inter-annotator quality control.
Strong analytical foundation in statistics and programming, with hands-on use of Python, SQL, and BI tools to support data accuracy, consistency, and model performance optimization.
Bachelor of Science in Statistics and Programming (2025), Machakos University.Rigorous training in statistical inference, data cleaning, experimental design, and programming equipped me to support end-to-end AI data pipelines. Coursework and practical projects emphasized structured and unstructured data handling, labeling schema design, inter-annotator agreement analysis, and quality control methodologies—directly applicable to large-scale text, image, and multimodal annotation tasks. Strong grounding in Python, R, SQL, SPSS, and STATA enables precise dataset validation, bias detection, and performance analysis of annotated corpora used in training and evaluating machine learning models.
