AI Evaluator
Image Annotation, Response evaluation, Rubric design, Data collection, and Response categorization.
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I am an experienced data analyst and AI data quality specialist with over 6 years of hands-on experience in data annotation, labeling, and evaluation for machine learning projects across health, development, and AI domains. My expertise includes annotating and curating datasets for NLP, computer vision, and generative AI models, as well as designing and executing prompt tests and model evaluations to ensure accuracy, fairness, and relevance. I have worked with tools such as Labelbox, Doccano, Python, and SQL, and have consistently delivered high-quality labeled data with >97% accuracy for large-scale AI and LLM projects. My background also includes leading QA processes, supporting multilingual and multimodal projects, and collaborating with cross-functional teams to improve dataset quality, reduce annotation error rates, and address bias and fairness in AI systems. I am passionate about leveraging data to drive reliable, ethical, and impactful AI solutions.
Image Annotation, Response evaluation, Rubric design, Data collection, and Response categorization.
Bachelor of Science, Applied Statistics with Computing
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