Senior Data Scientist

We are looking for an Applied Data Scientist - Credit Risk with strong expertise in Credit Risk Modelling, Machine Learning, Statistical Modelling, and Risk Analytics within the BFSI/Lending domain. The ideal candidate should have hands-on experience building and deploying credit scorecards, behavioural models, and risk solutions using advanced ML techniques.

Responsibilities:

• Develop and optimise Credit Risk Models, Application Scorecards, Behavioural 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.

Requirements:

• 4-7 years of experience in Data Science, Credit Risk Analytics, or Machine Learning within BFSI/Lending.
• Strong expertise in: Credit Risk Modelling, Application & Behavioural Scorecards, PD, LGD, ECL, BASEL-II & IFRS9 Credit Bureau Data.
• Hands-on experience with: Python, SQL, Pandas, Spark / PySpark, Scikit-Learn, XGBoost, TensorFlow / PyTorch.
• Strong understanding of: Logistic Regression, GLM, PCA, K-Means Clustering, Statistical Modelling & Machine Learning.

Skills

ML, Python, Credit Risk Modeling, Credit Risk Modelling, Machine Learning