Data engineering + statistical analysis + visualization

What This Demonstrates: End-to-end analytical workflow from data integration to insights:

• Multi-source data integration and transformation
• Time-series correlation analysis
• Statistical modeling of macro impacts on credit risk
• Executive-level visualization and documentation

Technical Implementation:

• Data Engineering (Databricks):
  • Ingested loan performance data + FRED macroeconomic indicators
  • Spark transformations for time-series alignment and aggregation
  • Feature engineering: YoY changes, moving averages, lag variables
  • Created analytical dataset for downstream analysis
• Analysis (Python + Tableau):
  • Correlation analysis between macro indicators and default rates
  • Regression modeling to quantify relationships
  • Scenario analysis for stress testing credit portfolios
  • Interactive Tableau dashboard with time-series visualizations
• Documentation:
  • White paper with methodology, findings, and recommendations
  • Executive summary with key insights
  • Statistical rigor and reproducible analysis

Credit Risk Macroeconomic Analysis.docx

Skills Showcased: ✓ Data engineering (Databricks/Spark) ✓ Multi-source data integration ✓ Statistical analysis (regression, correlation, time-series) ✓ Python (Pandas, Statsmodels, data transformation) ✓ Tableau (advanced visualizations) ✓ Business communication (white paper, documentation)

Real-World Context: At Fannie Mae, I perform similar macroeconomic risk analysis to support credit policy decisions. This demonstrates my ability to:

• Engineer complex datasets from multiple sources
• Apply statistical rigor to business questions
• Communicate technical findings to non-technical stakeholders

🔹 Case Study: Credit Risk Analysis White Paper (53K Loans, $718M Volume)

Link: Credit Risk Macroeconomic Analysis Dashboard