Quant Analyst

This handles the bread and butter of quantitative finance work: backtesting strategies, calculating risk metrics like VaR and Sharpe ratios, and running portfolio optimizations using Markowitz or Black-Litterman models. It's built around pandas and numpy for vectorized operations and pushes you toward realistic assumptions about transaction costs and slippage. The approach is sensible: data validation first, out-of-sample testing to catch overfitting, and risk-adjusted returns over raw performance. You'll get strategy implementations, backtest results with proper metrics, and sensitivity analysis. Good for pairs trading, options pricing, or any systematic strategy work where you need proper statistical rigor and don't want to reinvent the risk calculation wheel.