At State Street, I build AI applications and quantitative risk software that take econometric models from raw data to validation-ready documentation and evidence.
I architected a LangGraph multi-agent framework for automated model development and built a skill that generates Sphinx documentation from code-derived metadata.
I also enhance a REST API platform for financial models and data workflows, collaborating with frontend developers and end users to deliver a seamless experience. My work includes production code for CRE wholesale credit risk models and calculation engines used across portfolio types and regulatory exercises.
Previously, I developed models and led a Scrum team at UBS, following earlier work as an Economist at Moody's Analytics. I bring a PhD in Economics, an MSc in Statistics, and hands-on experience in Python-based software development, data science, and econometrics.
