Role Overview:

- 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.

Key Success Metrics:

- Model Accuracy & Risk Mitigation: Ensure the development and implementation of accurate credit risk models that effectively mitigate business and financial risk.

- Business Outcomes & Impact: Contribute to reducing default rates, enhancing credit approval accuracy, and driving profitability through advanced predictive analytics.

- Revenue Growth & Profitability: Drive revenue growth by ensuring that credit risk models enable profitable lending decisions and support sound credit policies.

- Model & Tool Development: Successfully develop and deploy credit risk models that align with business strategies and meet quality standards.

- Model Optimization & Enhancement: Continuously enhance model performance and efficiency through regular monitoring, back testing, and recalibration of models.

- Strategic Campaign Design: Independently design strategies for credit risk campaigns, including credit limit adjustments, delinquency management, and credit portfolio optimization.

- Tracking & Monitoring: Regularly track and report on the performance of credit risk models to ensure they meet business and risk management goals.

Experience & Skills:

- 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.