Applied Data Scientist – Credit Risk
Location: Gurgaon (Hybrid)
Experience: 4–7 Years
Role Overview
We are looking for an Applied Data Scientist – Credit Risk with strong expertise in Credit Risk Modeling, Machine Learning, Statistical Modeling, and Risk Analytics within the BFSI/Lending domain. The ideal candidate should have hands-on experience building and deploying credit scorecards, behavioral models, and risk solutions using advanced ML techniques.
Key Responsibilities
- Develop and optimize Credit Risk Models, Application Scorecards, Behavioral Models, PD, LGD, and ECL frameworks.
- Build predictive models using Logistic Regression, GLM, XGBoost, and Machine Learning techniques.
- Apply PCA, K-Means Clustering, and other analytical techniques for customer segmentation and risk assessment.
- Work with large-scale datasets, perform feature engineering, EDA, and model validation.
- Leverage Credit Bureau Data and lending datasets to improve risk decisioning.
- Ensure compliance with BASEL-II, IFRS9, and regulatory risk frameworks.
- Collaborate with Product, Risk, Engineering,



and Business teams to deliver production-grade AI/ML solutions.
- Mentor junior team members and contribute to model governance and best practices.
Required Skills
- 4–7 years of experience in Data Science, Credit Risk Analytics, or Machine Learning within BFSI/Lending.
- Strong expertise in:
- Credit Risk Modeling
- Application & Behavioral Scorecards
- PD, LGD, ECL
- BASEL-II & IFRS9
- Credit Bureau Data
- Hands-on experience with:
- Python
- SQL
- Pandas
- Spark / Py Spark
- Scikit-Learn
- XGBoost
- Tensor Flow / Py Torch
- Solid understanding of:
- Logistic Regression
- GLM
- PCA
- K-Means Clustering
- Statistical Modeling & Machine Learning
Good to Have
- Exposure to Generative AI, Agentic AI, RAG, LLMs, Prompt Engineering, and Model Governance.
- Experience with Deep Learning in financial services use cases.
Domain Preference: BFSI, Banking, Fin Tech, NBFC, Lending Platforms.

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