Mikhail Noskov

Mikhail Noskov is a member of our Retail and our AI, Insights, and Solutions practices.

Mikhail is a quantitative and machine learning specialist who works at the intersection of advanced analytics, software engineering, and business impact. He brings expertise in agentic methodologies, Bayesian algorithms, Monte Carlo methods, optimization under uncertainty, MLOps, and the development of machine learning products from concept through working software. He helps clients apply data science and AI to complex commercial and operational challenges, translating sophisticated models into practical tools for better decisions, stronger execution, and measurable results.

Mikhail studied at Yale University and has published and patented work in data-driven root-cause analysis, probabilistic graph models, and industrial-process analytics. Outside of client work, he follows health trends and enjoys skiing, paddleboarding, bicycling, sailing, and spending time in the gym. He also has a hands-on interest in pottery and a deep curiosity about the history of porcelain.