Job Summary:
We are looking for an experienced Data Scientist with strong expertise in credit risk modelling to develop, validate, and deploy data-driven models across retail credit portfolios. The role involves building scorecard and statistical models, performing advanced analytics, and partnering with business and technology teams to deliver scalable risk solutions.
Key Responsibilities:
Credit Risk Modelling & Analytics:
- Develop and maintain credit risk models, including scorecard models (PD, LGD, EAD)
- Build application, behavioral, and collection scorecards for retail credit products
- Perform exploratory data analysis, feature engineering, and segmentation on large datasets
- Conduct model performance monitoring, stability analysis, and back-testing
Model Development & Validation:
- Apply statistical and machine learning techniques for model development and calibration
- Perform model validation, including conceptual soundness, quantitative testing, and bench-marking
- Conduct sensitivity analysis, stress testing, and championchallenger evaluations
- Prepare comprehensive model documentation for governance and regulatory review
Model Deployment & Production:
- Support end-to-end model deployment from development to production
- Translate models into production-ready Python/SQL code
- Work closely with engineering teams to integrate models into decision systems
- Monitor post-deployment performance and recommend recalibration or redevelopment
Data & Technology:
- Use SQL to extract, join, and transform large-scale datasets
- Use Python for data analysis, modelling, automation, and reporting
- Ensure data quality, integrity, and consistency across the modelling lifecycle
Required Skills & Qualifications:
- 4+ years of experience as a Data Scientist with hands-on credit risk modelling exposure
- Strong experience in scorecard development and traditional statistical modelling
- Solid understanding of credit risk concepts (PD, LGD, EAD, delinquency, default, recovery)
- Proficiency in Python (pandas, numpy, scikit-learn or similar)
- Strong SQL skills for data extraction and analysis
- Experience across the full model lifecycle: development, validation, deployment, and monitoring
- Strong analytical, problem-solving, and communication skills
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