Lourenz Baliber

I’m a results-driven Data Scientist and Physics postgraduate with hands-on expertise in end-to-end machine learning pipelines, predictive modeling, and data-driven application deployment. I build solutions that move from analysis to production, combining strong modeling with practical deployment. I’ve developed and deployed ML systems including a heart stroke detection end-to-end pipeline using XGBoost with SHAP explainability—reaching 94.2% classification accuracy and 0.91 AUC-ROC—and packaging it into an interactive Flask + Streamlit web app for real-time risk prediction. I also optimize and engineer performance-focused systems, from a ray tracing renderer accelerated with BVH (about ~72% faster) to automation tooling that served 500+ users. Across my research roles, I’ve processed and analyzed large scientific datasets (e.g., 2,000+ fluorescence microscopy frames and ~8,000 data points), built Python/MATLAB workflows for passive particle tracking and rheological modeling, and collaborated remotely with international research teams (Philippines–Japan). I’m now seeking fully remote data science roles where analytical rigor meets real-world impact.