Credit Risk Modeller â?? Scoring Models | Advanced

Key Responsibilities:

• Design, build and implement credit scoring models (PD, LGD, IFRS 9) using structured and unstructured/alternate data.
• Develop machine learning and statistical models to enhance the credit vetting process.
• Conduct data mining, feature engineering, and statistical testing across large datasets.
• Work with risk, credit, analytics and technology teams to ensure models are integrated and performing optimally.
• Research and publish model documentation in alignment with governance and regulatory requirements.
• Engage with internal committees and influence modelling best practices across the business.

Ideal Background:

• PhD/MSc in Actuarial Science, Data Science, Quantitative Risk, or similar.
• 5+ years of hands-on experience in credit risk modelling, especially within a banking or financial services context.
• Strong foundation in GLMs, survival models, Markov models, or Cox-regression frameworks.
• Highly proficient in R, Python, SQL, and tools such as SAS or Emblem.
• Experience with R Shiny, dashboards, or model explainability tools will be a strong advantage.
• A track record of research publications, technical model development, and involvement in academic or industry collaboration projects is highly valued.

What Sets This Role Apart:

• Shape and influence the modelling roadmap across multiple credit portfolios.
• Join an award-winning team with a track record of innovation in IFRS 9 and survival modelling.
• Work with data science, analytics, and credit risk specialists in a high-impact environment.
• Exposure to cutting-edge research, alternate data strategies, and real-world deployment.
• Flexible hybrid setup with career progression and thought leadership opportunities.