Credit Risk Analytics Specialist

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Job Description

- Model Development : Design, develop, validate, implement, and maintain machine learning and statistical application and behavior scorecards for retail and non-retail customer segments.

- Data & Feature Engineering : Prepare, analyze, and transform structured and alternative data sources; develop predictive features and data frameworks to enhance credit risk assessment and model performance.

- Validation & Monitoring : Conduct model testing, periodic monitoring, annual validation, and calibration; identify model deterioration, model drift, and emerging risk signals to ensure ongoing model effectiveness and stability.

- Implementation & Deployment : Develop, code, deploy, and maintain machine learning and statistical models within credit decision systems; perform SIT/UAT, production releases, and post-implementation validation to ensure accurate and reliable model execution.

- Advanced Analytics & Innovation : Evaluate and apply emerging machine learning, AI, and analytical methodologies to improve predictive performance, model robustness, scalability, and analytical efficiency.

- Governance & Documentation : Maintain comprehensive model documentation, development evidence, monitoring reports, and validation materials; ensure compliance with model governance, audit, and regulatory requirements.

- Business Application & Portfolio Analytics : Translate model insights into credit strategies, risk-based segmentation, cut-off thresholds, customer review prioritization, and portfolio management actions to support business growth and risk management objectives.

- Stakeholder Management : Partner with business users, risk managers, model validators, and technology teams to gather requirements, communicate model insights and limitations, and drive continuous model enhancement.
We're committed to bringing passion and customer focus to the business.

If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us.