About Yan Yablonovskiy
Senior Analyst and Teaching Associate based in Melbourne, Australia. I work at the intersection of mathematics, machine learning, and software—building explainable, production-grade solutions and teaching statistics and engineering mathematics.
Profile & Highlights
- Senior Analyst (Fraud Prevention), National Australia Bank — card fraud detection and explainable rules.
- Teaching Associate, Monash University — statistics and engineering mathematics; hundreds of positive iSETU forms.
- PhD research in Statistical Mechanics (thesis under examination; submitted Oct 2024).
- Contributor to Google DeepMind’s research ecosystem.
- Focused tutoring in mathematics and computer science with applied outcomes.
Experience
- Senior Analyst, National Australia Bank (Dec 2022 – Present)
- Designed imbalanced XGBoost-derived decision-tree module to generate explainable fraud rules; received recognition award.
- Stack: SAS, Python, SQL, Drools rule engine; precision–recall trade-offs to minimize false positives.
- Teaching Associate, Monash University (Feb 2018 – Present)
- Undergrad tutorials/support: MTH2232 (Mathematical Statistics), ENG1005/ENG1090 (Engineering Mathematics).
- Assessment/marking for MTH5210 (Stochastic Calculus and Mathematical Finance).
- On-demand pure mathematics support at the Mathematics Learning Centre.
- Data Scientist, Commtel Network Solutions (Jun 2020 – Dec 2022)
- End-to-end ML: ETL/ELT, model training/deployment, serverless, dashboards (Power BI).
- Cloud & tooling: Python, TensorFlow/PyTorch, SQL (Kimball), AWS & Azure.
- Implemented research to synthesize network topology training data; collaborated with enterprise clients (Aurizon, Telstra).
- Built industrial IoT firmware (ESP32 in C) optimized for power consumption and data traffic.
Education
- Doctor of Philosophy, Statistical Mechanics, Monash University (2019 – 2024)
- Thesis under examination/revision; submitted Oct 2024.
- Bachelor of Science (Honours, 1st Class), Mathematics major, Monash University (2014 – 2018)
- Bachelor of Commerce, Finance major, Monash University (2014 – 2017)
Key Skills
- Mathematics: statistical modeling, regression, hypothesis testing, estimation, random processes, engineering mathematics.
- Programming: Python (OOP/AOP), SQL; production ML pipelines and MLOps on AWS/Azure; Power BI.
- Formal methods: strictly typed functional programming with Lean 4 (dependent type theory); digitizing pure mathematics results.
- Systems/IoT: ESP32 in C; low-power firmware and efficient telemetry.