Description:
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.
04 Jul 2025;
from:
gumtree.co.za