Situation:
Manual processes for daily excess and past-due credit product reporting were inefficient across 24 markets, impacting revenue collection and increasing turnaround time (TAT).

Task:
As Lead Data Engineer, I led a 5-person team to automate workflows, build dashboards, and integrate ML models to enhance credit risk decisions and business reporting.

Actions:

• Built automated workflows in Python/SQL for seamless data integration.
• Cleaned and engineered features for model training.
• Implemented and fine-tuned Random Forest models using precision and recall as evaluation metrics.
• Developed live Power BI dashboards for credit risk insights.
• Engaged stakeholders regularly to align on goals and feedback.

Result:
✅ Reduced TAT by 75%
✅ Optimized revenue collection by $500,000 (Q4 2022)
✅ Scaled across 24 markets
🏆 Received CEO Award – Agility Category