Credit decisioning,
from design to operation.
My role: architect and end-to-end technical owner.
From financial-data ingestion and model development to production delivery, evaluation, and ongoing operation.
I architected and own a production decisioning system that combines financial-data ingestion, transaction and behavioral feature engineering, calibrated ML scoring, explicit policy orchestration, explainability, versioning, and production monitoring.
Modeling
Productionized routed, calibrated risk models and engineered features from financial transactions and behavior. Evaluate discrimination and probability quality, and use champion/challenger evaluation to assess changes.
Decisioning
Keep model estimation separate from policy decisions, with explicit review paths and safe fallbacks. Integrate SHAP-based explanations and reason codes with versioned decision evidence to support review and investigation.
Production
Build and operate Python/FastAPI services using Docker and Azure. CI/CD, regression validation, versioned releases, and structured logging support controlled delivery and auditable decisions.
Lifecycle
Use monitoring, shadow evaluation, and outcome feedback to guide model and policy iteration. Connect investigation of input quality and model behavior with the next round of evaluation and engineering work.
The architecture overview explains my engineering responsibilities through a conceptual flow. The linked labs are independent synthetic demonstrations; they do not reproduce employer configurations, decision rules, or performance results.