Daniel D is a quantitative developer with 11 years of experience building data-driven systems at the intersection of finance and machine learning. After software roles at Google and Meta focused on Ads and ML optimization, he now develops alternative data solutions at Citadel, bringing production-grade engineering to quantitative research. He holds a BS in Finance from Wharton and an MSE in Computer Science from UPenn, a blend that helps him translate business signals into scalable models and infrastructure. Outside work he’s an artist and self-described “meme connoisseur,” a hint at a creative, culture-aware approach to problem solving.
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