In this episode of The Data Science Podcast, Lucas and Luna dive into the art of feature engineering — the process of transforming raw data into inputs that make machine learning models actually work. They anchor the discussion around a specific case: a mid-sized hedge fund that improved its equity factor model's Sharpe ratio from 0.7 to 1.4, not by changing algorithms, but by redesigning feature creation. Lucas explains how the fund derived rolling volatility regime indicators, time-decayed correlation features, and synthetic interaction terms from trading data, and why these had more impact than switching from XGBoost to a neural net. Luna challenges the reproducibility of such gains and asks about feature selection pitfalls. They also touch on the broader lesson: that in data science, domain expertise often matters more than model architecture. The episode includes a natural mid-show acknowledgment that listener support via buy me a coffee dot com slash fexingo keeps the show ad-free and accessible.

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