A practitioner in AI and quantitative finance. Researcher and educator on the side.

I’ve spent over eighteen years at the intersection of technology and financial markets, multi-asset and hedge funds, fintech and edtech startups, and the classroom. The thread through all of it: turning quantitative ideas into systems that actually run.

As director of a data-science consultancy, I lead teams that build and deploy analytics with measurable impact, quantitative trading models, risk-analytics platforms, and predictive investment frameworks across Python, R, SQL, and cloud architectures. I’ve worked on derivatives and multi-asset strategies informed by roughly $1.6 billion in assets under management, developing risk-factor models, allocation frameworks, and AI-driven decision systems built for scale.

I also teach. As a professor and advisor at a university in California, I cover machine learning, business analytics, quantitative finance, and data-driven real estate, and I’ve mentored 600+ students and professionals. That practitioner-academic blend is the whole point: I can derive the model on the whiteboard and ship the service that runs it. My book, Modern Analytics Engineering, is where the methodology lives.

My stance is simple and a little contrarian: most enterprise AI dies in the slide deck. The work that matters is the unglamorous engineering, data contracts, validation, monitoring, governance, that makes a model trustworthy in production. No jargon, just results.