ML Engineer

Weekday AI; Bengaluru; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

Weekday AI seeks a data-driven ML scientist with 2+ years in credit risk or fraud analytics to build risk models (PD/LGD/EAD) and scalable ML pipelines. You will translate business problems into measurable ML objectives, improve risk decisioning, and deploy models using tree-based methods on large structured financial datasets in Bengaluru.

Strong SQL and Python skills are essential. Experience with PySpark/Hive is a plus; the role emphasizes model evaluation, explainability, and collaboration

Qualifications

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

Responsibilities

• Develop risk models: PD, LGD, EAD and fraud detection.
• Translate business problems into ML objectives and targets.
• Improve risk decisioning, underwriting, and collections strategies.
• Develop scalable ML models using LightGBM, XGBoost, CatBoost.
• Feature engineering: WoE, IV, VarClus; model evaluation (AUC-ROC, F1).
• Data engineering: SQL, PySpark/Hive, large datasets.

Skills

Credit risk analytics

Python (Pandas, scikit-learn)

SQL (complex queries)

Tree-based models

Imbalanced data handling

Model evaluation metrics

Tools

PySpark

Hive

Distributed systems

Job description

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