Credit Risk Modeller – Scorecard Development

SUMMARY:
Are you a technically skilled Credit Risk Modeller with a deep understanding of scorecard development and a passion for solving complex problems using data? We are working with a leading financial services group that is shaping the future of credit decisioning. They are looking for a Credit Risk Modeller to take the lead in building robust credit scoring models using both traditional and alternative data sources, driving innovation across acquisition, account management, and collections strategies.

POSITION INFO:
Credit Risk Modeller – Scorecard Development

Key Responsibilities:

• Develop and implement credit scoring models (PD, LGD, and IFRS 9) across retail portfolios.
• Apply GLMs, survival analysis, and machine learning algorithms to build predictive, stable, and explainable models.
• Extract and manipulate large structured and unstructured datasets for modelling purposes.
• Conduct feature engineering, model validation, and performance monitoring.
• Collaborate with cross-functional teams across credit, risk, data science, and IT to deploy models into production.
• Lead documentation, governance, and presentation of models to internal risk committees.
• Contribute to research-led initiatives within credit analytics and advanced modelling techniques.

• PhD or MSc in Actuarial Science, Data Science, Applied Statistics, Quantitative Risk, or a related field.
• 5+ years’ hands-on experience in credit risk modelling in a financial institution or consulting environment.
• Strong proficiency in R, Python, SQL, and tools like SAS, Shiny, or Emblem.
• Expertise in statistical frameworks such as GLMs, Cox regression, Markov models, or survival analysis.
• Experience using alternative data sources (e.g. transactional data, bureau data, and behavioural data).
• A research mindset and a strong publication or presentation track record will be a distinct advantage.

• Shape the credit risk modelling roadmap across high-impact portfolios.
• Work on cutting-edge modelling techniques, including machine learning, scorecard optimisation, and explainability.
• Join a team known for innovation in IFRS 9, survival modelling, and data science-driven credit strategies.
• Flexible hybrid work model and strong career growth prospects.
• Collaborate in a research-rich, intellectually curious environment.