Operations Research / ML Analyst focused on applied machine learning and statistical modeling. Background in business analytics, quantitative finance, risk management, and model evaluation, with an emphasis on reliable, reproducible workflows.
Currently exploring LLMs and NLP.
- End-to-end MLOps pipeline for portfolio optimization and quantitative risk modeling.
- Financial mathematics and portfolio allocation using Black–Litterman and Risk Parity.
- Risk analysis using Value at Risk (VaR) and Conditional Value at Risk (CVaR).
- Reproducible workflows with MLflow and GitHub Actions.
- Option pricing using the closed-form Black–Scholes model.
- Numerical methods including finite differences and Monte Carlo simulation.
- Implied volatility, option Greeks, and interactive visualizations.
- Linear algebra, probability/statistics, calculus, and optimization.
- Mathematical foundations of machine learning models.
- Applications to quantitative finance, algorithmic trading, and time-series forecasting.
- Python fundamentals and data structures.
- Data science and Exploratory data analysis.
- Machine learning, deep learning, and system design.
- C and C++ programming exercises.
- Programming fundamentals and systems-level concepts.
