About
I’m a data scientist who enjoys turning messy, real-world data into decisions that matter. Right now, I’m pursuing my Master’s in Data Science at Drexel University, and I’ll be graduating in June 2025. But I’ve been in this space long enough to know it’s not just about models, it’s about making things work, especially when the data isn’t perfect and the deadline is tomorrow.
My path started in undergrad when I took a course on statistical modeling. It was the first time I saw how math and code could work together to predict the future, and I was hooked. That curiosity led to a bunch of side projects, like predicting NBA player career success (yes, I had a little too much fun with that), building a news article summarizer, and estimating employee compensation using regression trees. I didn’t know it then, but those projects set me on the track I’m on today.
Over time, I’ve grown into someone who can ship real solutions, not just notebooks. I’ve built forecasting dashboards that helped restaurant chains plan millions in revenue. I’ve worked with APIs, CRMs, Azure pipelines, and enough edge cases to know that production data science is as much about engineering and empathy as it is about algorithms.
My technical toolbox includes Python, SQL, R, PyTorch, XGBoost, Docker, Azure, and Streamlit, and I’m certified as an Azure AI Engineer and Data Scientist. But what really excites me now is explainable AI, real-time inference (like Triton Server deployments), and making AI more transparent and accessible.
Whether I’m mentoring students at Drexel, debugging ETL pipelines at 2 a.m., or building a resume generator from scratch (yes, I did that too), I’m always learning. My goal is simple: join an AI-first company where I can build things that make people’s lives easier, even if they never see the code behind it.
If you’re working on something in AI, fintech, healthcare, or just solving hard problems with data, I’d love to connect.