About Me
I am a data scientist focused on modeling and forecasting problems that support high-impact decision making. I am motivated by turning uncertainty into actionable insight, where analysis directly influences real-world outcomes.
My background spans applied machine learning, time series forecasting, and data engineering. I have built production forecasting systems for energy markets, analytical pipelines for quantitative trading, and conducted research in multimodal deep learning. My work emphasizes robustness, interpretability, and deployment at scale.
I follow a “business before ML” approach: define the decision problem first, build strong data foundations, and use the simplest effective models before adding complexity. I am particularly interested in forecasting, optimization, and decision-making under uncertainty.
My long-term goal is to work as a quantitative researcher or machine learning engineer on technically deep problems with clear economic impact.
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