MSc Actuarial Science · Quantitative Finance & Data Science
Turning complex mathematical frameworks into scalable, data-driven solutions across finance, insurance, and analytics.
Applied mathematician with an MSc in Actuarial Science and a specialization at the intersection of quantitative finance, machine learning, and insurance analytics. My work focuses on building rigorous statistical models and deploying them into production-ready systems — from derivatives pricing and risk management to predictive underwriting.
| Category | Tools |
|---|---|
| Languages | Python · R · SQL · MATLAB |
| ML / DS | Pandas · NumPy · Scikit-learn · TensorFlow · PyTorch |
| Visualization | Matplotlib · Seaborn · Plotly · Tableau · Power BI |
| Finance | Bloomberg Terminal · QuantLib · Backtrader |
| Domain | Methods |
|---|---|
| Machine Learning | Regression · Classification · Time Series · Deep Learning |
| Statistics | Monte Carlo Simulations · Bayesian Inference · Hypothesis Testing |
| Quant Finance | Asset Pricing · Risk Management · Derivatives Pricing |
📌 Reinforcement Learning for algorithmic trading strategies
📌 Advanced Deep Learning architectures (PyTorch / TensorFlow)
📌 ML-driven insurance underwriting optimization
I'm actively looking for projects at the intersection of:
- Quantitative Finance — risk modeling, portfolio optimization, derivatives
- Actuarial Analytics — predictive modeling, underwriting, reserving
- Sports Analytics — performance modeling and data-driven decision making
- Business Intelligence — advanced statistical frameworks for actionable insights
⭐ If you find my work useful, a star on any of my repositories is appreciated.