Key Responsibilities :

Risk Modeling &

- Business Impact :
- Build and deploy models for : Probability of Default (PD), Loss Given Default (LGD), Exposure at Default (EAD), and Fraud detection and capture rate optimization.
- Translate business problems into measurable ML objectives and target variables.
- Drive improvements in risk decisioning, underwriting, and collections strategies.

Machine Learning &

- Model Development :
- Develop scalable ML models using : LightGBM, XGBoost, CatBoost, Random Forest, CART, and Logistic Regression.
- Work extensively on tabular datasets (structured financial data).
- Build ensemble and stacking models for improved performance.

Feature Engineering &

- Model Evaluation :
- Perform advanced feature engineering using : Weight of Evidence (WoE), Information Value (IV), and Variable Clustering (VarClus).
- Evaluate models using : AUC-ROC / Gini coefficient and F1 Score, Precision, Recall.
- Handle class imbalance using : SMOTE, Class weighting, and Threshold tuning.

Model Optimization &

- Explainability :
- Optimize models using : Grid Search / Random Search and Bayesian Optimization (Optuna preferred).




- Ensure model interpretability using : SHAP values, LIME, and Partial dependence plots.
- Communicate model insights effectively to business and risk stakeholders.

Data Engineering &

- Pipeline Development :
- Process large-scale datasets using : SQL (advanced level mandatory) and PySpark / Hive / distributed systems.
- Build robust data pipelines for model training and deployment.
- Work with large transactional or bureau datasets.

Required Skills &

Experience :

Must-Have :

- 2 - 5 years of relevant experience in credit risk / fraud analytics.
- Robust hands-on experience with : Python (Pandas, Scikit-learn) and SQL (complex queries, optimization).
- Expertise in tree-based models (XGBoost/LightGBM).
- Experience with imbalanced datasets in financial use cases.
- Strong understanding of model evaluation metrics beyond accuracy.

Good to Have :

- Experience with : PySpark / distributed computing, Credit bureau / transactional datasets, and Fintech / NBFC / banking domain.

📌 Machine Learning Engineer - Credit Risk (India)
🏢 Neemtree
📍 India