Michael Frasco is a Staff Machine Learning Engineer with 11 years of experience applying forecasting, causal inference, and optimization to high-impact business problems. He has a track record of translating research-grade models into production systems that drove measurable savings—$100M at DoorDash and $40M at Convoy—by optimizing marketplace bidding and marketing spend. Equally comfortable with distributed pipelines, anomaly detection, and Bayesian experimentation, he bridges product strategy, finance, and engineering to ensure models are actionable and auditable. His academic training in statistics from the University of Chicago underpins a rigorous approach to uncertainty quantification and experiment design. Based in Seattle, he enjoys tackling end-to-end ML problems where forecasting meets constrained optimization and decision-making. A detail that often goes unnoticed: he pairs deep modeling skills with hands-on deployment experience, owning feature engineering through live model rollouts.
11 years of coding experience
9 years of employment as a software developer
Master’s Degree Statistics, Master’s Degree Statistics at University of Chicago
Contributions:1 PR, 18 pushes, 1 branch in 1 year 7 months
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