ML Engineer

The candidate will have responsibilities across the following functions:
Risk Modelling and Business Impact:
Build and deploy models for:
Probability of Default (PD)
Loss Given Default (LGD)
Exposure at Default (EAD)
Fraud detection and capture rate optimisation.
Translate business problems into measurable ML objectives and target variables.
Drive improvements in risk decisioning, underwriting, and collections strategies.
Machine Learning and 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
Feature Engineering and 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
Model Optimisation and Explainability
Optimise models using:
Grid Search / Random Search
Bayesian Optimisation (Optuna preferred)
Ensure model interpretability using:
SHAP values
LIME
Partial dependence plots
Communicate model insights effectively to business and risk stakeholders
Data Engineering and 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
Requirements:
2 - 5 years of relevant experience in credit risk/fraud analytics.
Strong hands-on experience with:
Python (Pandas, Scikit-learn)
SQL (complex queries, optimisation)
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

Skills
AWS, Backend Engineers, CI/CD, Databricks, Machine Learning

Experience
2-6 yrs