# ML Engineer

> Weekday AI · Bengaluru, India · Full-time · Posted 2026-09-09

**Workplace:** on_site

**Department:** Weekday's Client via platform

## 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

## Apply

[Apply at Weekday AI](https://apply.workable.com/weekday-1/j/443BC75B03/apply)

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