ML Engineer
This role is for one of Weekday’s clients
Min Experience: 2+ years
Location: Bengaluru
JobType: full-time
Requirements
Key Responsibilities
1. Risk Modeling & Business Impact
Build and deploy models for:
Probability of Default (PD)
Loss Given Default (LGD)
Exposure at Default (EAD)
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
2. Machine Learning & Model Development
Develop scalable ML models using:
LightGBM, XGBoost, CatBoost
Random Forest, CART, Logistic Regression
Work extensively on tabular datasets (structured financial data)
Build ensemble and stacking models for improved performance
3. Feature Engineering & Model Evaluation
Perform advanced feature engineering using:
Weight of Evidence (WoE)
Information Value (IV)
Variable Clustering (VarClus)
Evaluate models using:
AUC-ROC / Gini coefficient
F1 Score, Precision, Recall
Handle class imbalance using:
SMOTE
Class weighting
Threshold tuning
4. Model Optimization & Explainability
Optimize models using:
Grid Search / Random Search
Bayesian Optimization (Optuna preferred)
Ensure model interpretability using:
SHAP values
LIME
Partial dependence plots
Communicate model insights effectively to business and risk stakeholders
5. Data Engineering & Pipeline Development
Process large-scale datasets using:
SQL (advanced level mandatory)
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
- Strong hands-on experience with:
- Python (Pandas, Scikit-learn)
- 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
Fintech / NBFC / banking domain
Good-to-have skills
Machine Learning, Credit Risk, Credit Risk Management