FinTech ML Engineering Β· Credit Risk Β· Fraud Detection Β· Systematic Trading CS @ Brooklyn College Β· Open to FinTech roles
| Project | Stack | Description |
|---|---|---|
| Quantitative Trading Framework | Backtrader Β· QuantStats Β· yfinance Β· Plotly Dash | Momentum + mean-reversion signals backtested over 4 years. Sharpe 0.74. Walk-forward validated, real transaction costs. |
| Credit Risk Scoring Engine | XGBoost Β· SHAP Β· FastAPI Β· PostgreSQL Β· Docker | End-to-end loan default prediction. ROC-AUC 0.79, 307K records, REST API with adverse-action SHAP explanations. |
| Fraud Detection Engine | Kafka Β· Redis Β· XGBoost Β· FastAPI Β· PostgreSQL | Real-time transaction scoring at 2ms. ROC-AUC 0.98, 89% fraud recall, 0.08% false positive rate. Built solo in 8 weeks. |
- Building β Quantitative trading framework with walk-forward validation across 500+ S&P 500 tickers
- Reading β Advances in Financial Machine Learning by Marcos LΓ³pez de Prado
- Exploring β How production ETL patterns translate to real-time market data pipelines: TimescaleDB vs kdb+



