Lead Data Scientist with 7 years of experience building production-scale forecasting, pricing, and decision-support systems for Fortune 50 retailers. Expertise in demand forecasting, inventory planning, markdown optimization, anomaly detection, and new-item forecasting. Proven track record of delivering scalable ML solutions that improve accuracy, reduce compute cost, and support high-impact merchandising and supply-chain decisions.

I also build Agentic AI solutions, creating products that connect external signals with internal insights, uncover correlations, and generate actionable statistics. These complement my forecasting work and reflect my broader interest in bridging traditional machine learning with emerging AI capabilities.

My approach to problems is straightforward: find the simple solution that gets 90-95% of the results, ship it, and iterate x iterate. I'd rather ship something that works than chase perfection first. Progress comes from quick feedback and continuous refinement.

Outside of work, I'm drawn to physics and evolution. I like understanding the principles and math behind how things work, which overlaps a lot with machine learning. It keeps my curiosity alive and gives me new ways to think about problems.