Senior ML Modeler Credit Risk, Collections
2 weeks ago
Job Summary:
We are hiring an experienced ML Modeler / Lead with strong hands-on expertise in developing, validating, and deploying machine learning models in the banking & financial services domain. The role focuses on credit risk models, acquisition & behavioral scorecards, collections strategies, and fraud detection models.
The ideal candidate is a hardcore ML modeler who has built and implemented XGBoost, Decision Tree, and Ensemble models for BFSI, and can take end-to-end ownership from data preparation to deployment & monitoring.
Key Responsibilities:
- Develop, implement, and validate ML models for credit risk, collections, behavior scorecards, and fraud detection.
- Build acquisition scorecards and customer lifecycle models for retail banking.
- Work on collections strategies, ECM, HRAM models to drive business impact.
- Hands-on development using Python, SQL, Scikit-learn, TensorFlow, PyTorch.
- Apply ML techniques (XGBoost, Decision Trees, GBM, Random Forest, Regression, etc.).
- Deploy models into production environments (Cloud / On-Prem) and monitor performance.
- Partner with model validation and MRM teams to defend models during audit.
- Lead/mentor junior data scientists and contribute to innovation in BFSI analytics.
Desired Skills & Experience:
- 8+ years of applied ML model development in banking / financial services domain.
- Proven track record in credit risk modeling, collections, fraud detection, and behavioral scorecards.
- Strong Python, SQL, ML libraries (Pandas, Numpy, Scikit-learn, TensorFlow, PyTorch).
- Hands-on with XGBoost, Decision Trees, Ensemble Methods.
- Experience with MLOps tools (MLflow, Airflow, Docker, Kubernetes) preferred.
- Excellent understanding of retail banking strategies underwriting, ECM, HRAM, recoveries.
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