Applied Data Scientist

Questhiring; Gurugram District; On-site; INR 2,250,000 - 2,750,000; Full time

Job summary

Questhiring is seeking an Applied Data Scientist with strong expertise in Credit Risk Modeling to build scalable AI/ML solutions for lending and risk decisioning.

The role involves developing PD, LGD, and ECL models, deploying scorecards, and applying ML methods such as Logistic Regression, XGBoost, and deep learning, with data processing in SQL and Spark. You will collaborate with product and engineering teams, improve governance, and mentor junior staff.

Qualifications

• 4-7 years in Applied Data Science, Risk Analytics, or Credit Risk Modeling within BFSI/Lending.
• Strong 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.

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.

Skills

Python

SQL

Scikit-learn

XGBoost

TensorFlow

PyTorch

Logistic Regression

GLM

PCA

K-Means

Tools

Spark

PySpark

Job description

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.
• Strong 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
• ML Libraries: Scikit-learn, XGBoost, TensorFlow, PyTorch
• Modeling: Logistic Regression, GLM, XGBoost, PCA, K-Means