Adept technical know-how: - Python (NumPy, SciPy, pandas, statsmodels, scikit-learn, Seaborn, Plotly, Dash, numba, Dask, SQLAlchemy, pymc3, QuantLib, arch, TensorFlow-Probability, cvxpy) - R (tidyverse, data.table, mlr, caret, Prophet, randomforest, rugarch, rmgarch, factorstochvol) - Julia (currently learning) - SQL Financial risk professional with 13+ years of experience in trading, quantitative risk management, and predictive data analytics. Deep understanding of energy and capital markets focusing on electricity, natural gas, equity, and high-yield credit. Advanced skillsets in derivatives pricing, asset valuation, portfolio optimization, hedge analysis, volatility estimation/forecasting, dependence structure modeling (e.g., w/ copulas), and market risk measurement (e.g., VaR, conditional VaR, cash flow-at-risk, gross margin-at-risk, stress testing, sensitivity analysis). Well-versed in multiple linear regression, robust regression, Bayesian inference, and time series analysis (e.g., ARIMA, SARIMAX, GARCH, BSTS). Additional competencies include stochastic programming, Monte Carlo methods, bootstrapping, dimensionality reduction (e.g., PCA, PPCA, kernel PCA, ICA, projection pursuit, factor analysis), regime-switching regression, Hidden Markov Models (HMMs), Gaussian Processes (GPs), random forests, gradient boosting, and deep learning. ©