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Data Scientist fueled by a long-standing obsession with mathematical patterns. My expertise spans Machine Learning, Deep Learning, and Statistical Analysis, but my approach is shaped by my background as a basketball player. That experience instilled a team-first mentality and a drive for precision that I bring to every model I build. I specialize in transforming messy, high-dimensional data into clear, strategic insights.

My primary workflow involves Python and PyTorch for model development, supported by PostgreSQL for robust data management. To ensure these models perform in production, I utilize Docker for containerization and Apache Airflow for workflow automation, all hosted and scaled within the AWS ecosystem.

I'm at my best when problems don't fit neatly into boxes. Give me a business challenge and I'll move fluidly between data exploration, model building, and engineering, whatever it takes to solve it right. I'm frustrated by metrics that feel disconnected from reality, so I obsess over alignment: making sure we're measuring what matters, that technical decisions ladder up to business outcomes, and that insights actually translate into action. I thrive in cross-functional environments where I can influence how problems are shaped, not just solved.