Parsippany, New Jersey, United States
Developed a full-stack predictive modeling tool (Python, Flask) with 98% accurate XGBoost models to assess roofing system moisture risk, which includes an optimization feature that generates superior alternative configurations
Reduced roof moisture risk assessment time from multi-day hygrothermal simulations (WUFI) to seconds, enabling contractors and internal teams to make significantly faster, data-driven decisions, benefiting sales and customers
Automated internal newsletter publishing workflows by integrating LLM capabilities with Google Drive APIs, streamlining monthly Systems Engineering newsletters and reducing manual preparation time
Conducted CAD comparison study of fastener plate designs using Keyence VL to assess plate manufacturing accuracy.