Job Description:
Key Responsibilities:
- Design & Develop Credit Risk Scorecards: Develop, build and maintain credit risk models and scorecards, leveraging machine learning techniques to assess customer creditworthiness.
- Data Handling & Preprocessing: Perform data cleansing, merging, and enrichment, ensuring high-quality datasets for scorecard development. Handle large-scale financial datasets and transform them into actionable insights for credit risk scoring.
- Feature Engineering: Extract and engineer meaningful features from raw financial data to enhance the predictive power of credit risk models. Perform advanced feature selection techniques to optimize model performance.
- Risk Model Development: Build, test, and refine machine learning algorithms to predict credit risk, leveraging a mix of traditional and advanced analytical approaches.
- Model Evaluation & Monitoring: Ensure robust validation and back testing of credit risk models to guarantee accuracy and regulatory compliance. Continuously monitor model performance and recalibrate models as needed to align with business goals.
- Regulatory Compliance & Transparency: Ensure that all credit risk models comply with industry regulations and internal standards. Use best practices for model governance, ensuring transparency and traceability throughout the process.
- Business Insights & Reporting: Conduct exploratory and targeted data analyses to gain actionable insights and support the development of new strategies for assessing and managing credit risk.
- Model Performance Tracking: Continuously track the performance of deployed credit risk models, analyse any deviations, and ensure models meet business expectations and regulatory standards.
Ideal Profile:
- Proven experience in developing and deploying credit risk models (e.g., Application scoring, behavioural scoring).
- Solid understanding of credit risk assessment techniques, including statistical, machine learning, and traditional risk modelling approaches.
- Experience in using data science and statistical software (e.g., Python, Pyspark) for data analysis and model development.
- Strong communication skills, with the ability to present complex analytical results to non-technical stakeholders.
- Experience in model deployment practices.
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