I am an aspiring Risk Analyst who turns financial and operational data into clear, decision-focused insights. My portfolio demonstrates probability-of-default modeling, expected-loss analysis, property investment underwriting, model governance, explainability, and stress testing.
I am currently open to entry-level opportunities in Risk Analytics, Credit Risk, Financial Analytics, and Data Analytics.
An end-to-end credit-risk workflow covering leakage-aware PD modeling, out-of-time validation, probability calibration, risk grades, expected loss, cutoff strategy, explainability, and population-stability monitoring.
- Application-model AUC: 0.727
- Gini: 0.454
- KS: 0.330
- Top-10% risk cutoff retained approximately 89.6% approval while reducing modeled expected loss by approximately 29.8%
A property-investment screening workflow combining out-of-fold fair-value estimation, valuation-risk indicators, cap rate, LTV, DSCR, cash-on-cash return, NPV, IRR, and downside stress scenarios.
- Risk analytics: PD, expected loss, risk grading, cutoff strategy, stress testing, model monitoring, governance, and explainability
- Programming and data: Python, Pandas, NumPy, SQL, Jupyter Notebook
- Modeling: Logistic Regression, Random Forest, Gradient Boosting, calibration, classification and regression metrics
- Business tools: Excel, data visualization, portfolio analysis, and executive reporting
- Deployment: GitHub, Streamlit Community Cloud
- Translate technical results into practical risk decisions
- Explain assumptions, limitations, and model-governance controls
- Build reproducible analyses and interactive decision-support applications
- Communicate findings clearly to both technical and business stakeholders