Senior Solution Architect, Machine Learning For Anomaly Detection at Kaspersky
Moscow, Moscow City, Russia
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Summary
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Senior
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Top School
Maxim Mamaev is a Senior Solution Architect specializing in machine learning-driven anomaly detection for industrial equipment, with eight years focused on productionizing real-time telemetry analytics. At Kaspersky he leads client engagements and designs data stream processing and diagnostic-rule modules for MLAD, combining neural networks, statistics and rule-based diagnostics to deliver early warnings in energy, oil & gas and metallurgy. He previously founded Ada, building a serverless multivariate anomaly detector, and earlier grew product lines and technical teams as a technical director, giving him rare end-to-end experience from research-grade models to commercial deployment and sales. Trained as a physicist (MS, Novosibirsk State University), he pairs strong mathematical intuition with hands-on skills in Python, TensorFlow, Kafka and Docker. Fluent in English (IELTS 8.0), he also regularly presents at conferences and performs on-site integrations, reflecting both customer-facing instincts and deep implementation know-how. An often-overlooked strength is his ability to translate complex diagnostic signals into actionable rules and marketing narratives that drive adoption in conservative industrial environments.
8 years of coding experience
18 years of employment as a software developer
Master of Science - MS, Physics, graduated with honors, Master of Science - MS, Physics, graduated with honors at Novosibirsk State University (NSU)
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