Jeremy Schmitt is a Sr. Staff Data Scientist and mathematician with 11 years of experience applying rigorous mathematical modeling and efficient algorithm design to production ML systems. He blends a PhD-level appreciation for abstract math with hands-on experience deploying real-world solutions—most notably an approximate algorithm to tackle an NP-hard pooling vehicle routing problem at Flock Freight that reduces emissions and improves marketplace economics. His background includes leading fraud-detection efforts at FICO, where he engineered low-latency, low-false-positive models informed by cross-disciplinary teams and rapid adversary adaptation. Jeremy is comfortable moving ideas from theory to scalable code, with a passion for scientific computation and mathematical modeling evident across consulting and academic roles. Based in Encinitas, CA, he brings a rare mix of deep academic training and practical product impact that favors elegant, efficient algorithms over brute-force solutions.
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