Credit Risk Data Scientist
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Key Responsibilities
- Develop, enhance, and monitor credit risk models across the customer credit lifecycle.
- Conduct portfolio performance analysis to identify trends, emerging risks, opportunities, and key risk drivers.
- Perform exploratory data analysis, segmentation, feature engineering, and model performance assessments.
- Support model validation, calibration, back-testing, and governance processes.
- Design, develop, and maintain automated reporting solutions and management dashboards.
- Build and enhance business intelligence tools to support portfolio monitoring and decision-making.
- Extract, transform, validate, and analyse large datasets from multiple sources.
- Produce regular and ad hoc risk reports, investigations, and analytical insights.
- Support the optimisation of credit risk strategies through data-driven recommendations.
- Investigate data quality concerns and contribute to remediation initiatives.
- Partner with business and technical stakeholders to deliver analytical and reporting solutions.
- Drive process improvements, automation initiatives, and enhanced risk monitoring capabilities.
Requirements
Education
- Degree in Statistics, Mathematics, Data Science, Computer Science, Engineering, Actuarial Science, Economics, Finance, or a related quantitative field.
Experience
- 3 to 6 years' experience within Credit Risk Analytics, Data Science, Quantitative Analytics, Risk Modelling, or a similar analytical environment.
- Experience within Banking, Financial Services, Lending, FinTech, or Credit Risk environments.
- Proven experience developing, monitoring, or enhancing predictive models, scorecards, or risk modelling frameworks.
- Strong exposure to portfolio analytics, performance monitoring, and data-driven decision-making.
- Experience working with large, complex datasets to generate meaningful business insights.
- Exposure to model performance tracking, validation, and governance processes.
- Experience developing automated reporting solutions and analytical dashboards.
Technical Skills
- Advanced SQL for data extraction, transformation, and analysis.
- Strong programming skills in Python, SAS, or similar analytical tools.
- Experience with Power BI, Tableau, or other data visualisation platforms.
- Solid understanding of predictive modelling, statistical techniques, and quantitative analysis.
- Knowledge of credit risk concepts including scorecards, portfolio management, provisioning, and model performance measurement.
- Experience with reporting automation and process optimisation will be advantageous.
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