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Mathematics, Statistics, and Data Analysis

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Freelancer Overview

Although I only have entry-level experience with data labelling, my strong foundation in data science, AI-driven solutions, and quantitative analysis brings a solid understanding of how AI training models work. My academic background in Natural Sciences with a focus on Mathematics, Statistics, and Molecular and Cell Biology has equipped me with skills in handling complex datasets, making me proficient in Python, data structures, and computational modelling. I have also cultivated advanced analytical skills and awareness of various types of deep learning techniques through my projects on synthetic microbial community design and machine learning for early cancer detection. Furthermore, my work experience, including roles as an AI Analyst and Spatial AI Entrepreneur Intern, has provided practical exposure to AI-driven projects. I’ve worked on real-world applications, contributing to AI model optimisation and leveraging cutting-edge technology for data analysis. Thus, I am able to contribute both my theoretical knowledge and practical AI experience effectively to data annotation, even at the entry level.

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