Role Overview

We are seeking an Applied Data Scientist with strong expertise in

Credit Risk Modeling to build scalable AI/ML solutions for lending and risk decisioning. The ideal candidate should have hands-on experience in

BFSI/Lending

, statistical modeling, machine learning, and credit risk frameworks.

Key Responsibilities

- Build and deploy

Application Scorecards, Behavioral Models, and Portfolio Risk Models

.

- Develop

PD, LGD, and ECL models aligned with

BASEL II and

IFRS9

.

- Apply

Logistic Regression, GLM, XGBoost

, and other ML techniques for credit risk.

- Perform feature engineering, EDA, and large-scale data processing using

SQL, Spark/PySpark

.

- Use

PCA and

K-Means for customer segmentation and risk analysis.

- Collaborate with Product, Engineering, and Business teams to deliver production-ready ML solutions.

- Improve model performance, explainability, and governance.





- Mentor junior team members and contribute to ML best practices.

Required Experience

- 4–7 years in

Applied Data Science, Risk Analytics, or Credit Risk Modeling within

BFSI/Lending

.

- Robust experience with

Credit Bureau data

, scorecards, behavioral models, and regulatory risk frameworks.

- Hands-on knowledge of

Machine Learning, Statistical Modeling, and Deep Learning

.

- Exposure to

Generative AI, RAG, Agentic AI, Prompt Engineering, or LLMs is a plus.

Technical Skills

- Programming:

Python, SQL

- Data Processing:

Pandas, Spark/PySpark

- ML Libraries:

Scikit-learn, XGBoost, TensorFlow, PyTorch

- Modeling:

Logistic Regression, GLM, XGBoost, PCA, K-Means

- Domain:

Credit Risk, PD, LGD, ECL, BASEL II, IFRS9, Credit Bureau Data

📌 Applied Data Scientist (Gurugram)
🏢 Questhiring
📍 Gurugram