Closing Remarks

data scientist with strong expertise in machine learning, cloud data engineering, and applied AI. Built and deployed large-scale data pipelines and ML models across domains including financial analysis, fraud detection, and natural language processing. Skilled in Python, SQL, and AWS (Lambda, Redshift, Glue, Step Functions), I combine hands-on engineering with deep theoretical grounding in probability, statistics, and machine learning. My main strength lies in my grounding in mathematics and personal study of physics, which provides a rigorous foundation for my work in artificial intelligence and machine learning. Pulling from probability theory, linear algebra, optimization, and mechanics, he brings a intuition for modeling complex systems and formal reasoning. He has experience with entity resolution, predictive modeling, survival/hazard models, and deep learning architectures, as well as MLOps practices for scalable deployment. Beyond implementation, Sabelo actively engages with the theoretical side of ML, studying Bayesian inference, statistical learning theory, and geometric deep learning. He has also contributed to projects involving economic impact modeling, OCR and handwriting recognition, and NLP pipelines for document summarization and analysis. With a strong foundation in mathematics, data engineering, and AI research.