Agbozu Ebingiye Nelvin is a Nigeria-based data scientist and AI evaluation specialist working across environmental analytics, geospatial data, and machine-learning quality assurance. He is an author and co-author of the 2026 Springer Nature open-access study, A hybrid AI model integrating LSTM, XGBoost, and K-means for interpretable prediction and clustering of water quality in data-scarce regions. His work focuses on data quality, interpretable modelling, reproducible analysis, edge-case evaluation, and decision-support systems for complex, data-limited environments.