“Hello_World”

I’m a Director of Data Science at a Fortune 500 fintech company, with a career that spans both Wall Street investing and modern data science engineering. My work lives at the intersection of finance, analytics, and AI, this is where I believe judgment, uncertainty, and real-world constraints matter as much as models and code.

Before formally transitioning into data science, I spent over two decades in capital markets as a quantamental equity research analyst, sales trader, and investment finance specialist. I’ve built and evaluated investment theses across equities and derivatives at a global investment bank and at a multi-billion-dollar, event-driven hedge fund. That experience deeply shaped how I think about data: not as an abstract exercise, but as a tool for decision-making under uncertainty.

Over the last several years, I’ve focused on bringing those investing instincts into production-grade analytics and machine learning systems. I’ve worked hands-on across the full data science lifecycle (data wrangling, feature engineering, statistical modeling, machine learning, and communicating results to technical and non-technical stakeholders). My work emphasizes explainability, robustness, and business impact, not just model performance.

At the core of my approach is quantamental thinking: blending quantitative rigor with qualitative insight. Turning messy, real-world data into signals, as well as turning signals into action, this has been the consistent theme of my career, whether the outcome is an investment decision, a product insight, or an AI-driven recommendation.

I recently completed my Master of Science in Data Science at Northwestern University, with a specialization in Machine Learning and AI. I’m also IBM-certified in Data Science and continue advanced coursework in AI Engineering, while pursuing CFA Level II. More importantly, I continue to learn by building—experimenting with agentic AI systems, applied machine learning, and data products that operate at scale.

This blog is a place where I share:

Practical data science techniques grounded in real financial problems
Lessons from bridging investment intuition with ML and AI
Experiments, frameworks, and architectures I’m actively exploring
Perspectives on how data science actually creates value in finance and fintech

Have fun browsing—and if something here sparks an idea, a debate, or a collaboration, I’d love to connect.

My coding projects are available at my G ithub page.

Let’s get in touch via email or connect via LinkedIn or Twitter to see how your data can tell a story, identify investment strategies, or if you just want to collaborate.