Ognjen Raketic
Dual-Master engineer bridging heavy industry and quantitative risk modeling. I integrate stochastic calculus, high-frequency telemetry, and machine learning to predict systemic failures, optimize operations, and actively protect corporate EBITDA.
Industrial Operations & Optimization
• 12 years of leading engineering & technical teams (70+ employees) in heavy industry.
• Preserved €12.97M in EBITDA by deploying real-time predictive stochastic models (mSIPOC 2.0).
• Permanently increased OEE from 58% to 79% using Lean, SMED, and SAP integrations.
Quantitative Analysis & Tech
• Executive Bootcamp at PoliMi GSoM
• McKinsey Forward Program – Certified in McKinsey's Problem Solving Toolkit (SMART frameworks, MECE Issue Trees, and Prioritization Matrices).
• M.Sc. in Computational Finance (RAF) & M.Sc. in Industrial Engineering (FTN).
• Designed C++/Python stochastic kernels validating over 30 million Monte Carlo scenarios.
• Developed automated compliance & carbon risk systems (EU ETS & CBAM risk simulator).
Alfa-Pulse Framework
Mathematical predictive model based on a stochastic Hawkes-Merton kernel. Validated through 30 million simulated scenarios (NASA & PRONOSTIA datasets). Demonstrates a stable logical intervention window of up to 46.83 hours before physical failure.
mSIPOC 2.0
Real-time prescriptive profit analytics and zero-touch orchestration designed to protect corporate EBITDA. Fully validated on live industrial systems and accepted for presentation at the Future-BME 2026 conference.
CBAM & EU ETS CALCULATOR
Interactive financial risk simulator built in Python/Streamlit. Translates raw industrial emissions metrics directly into corporate financial hedging strategies to mitigate European carbon tax liabilities.