Credit Risk Modeler

We are looking for a skilled Credit Risk Modeler to design, develop, validate, and optimise credit risk models across the customer credit lifecycle. The successful candidate will apply advanced statistical techniques, machine learning methodologies, and artificial intelligence solutions to generate actionable business insights and improve risk decisioning processes.

The ideal candidate will possess strong quantitative skills, experience in Probability of Default (PD), Loss Given Default (LGD), and Exposure at Default (EAD) modelling, and the ability to communicate complex analytical findings to both technical and business stakeholders.

Experience with cloud-based analytical platforms, particularly Google Cloud Platform (GCP), is highly desirable.

• Develop, enhance, and validate credit risk models, including PD, LGD, and EAD models.
• Apply advanced statistical, machine learning, and artificial intelligence techniques to improve model performance and business outcomes.
• Build and validate analytical solutions using GCP, Python, SAS, SQL, and R.
• Analyse modelling outputs and translate findings into actionable business recommendations.
• Continuously improve modelling methodologies and analytical processes.
• Prepare comprehensive model documentation, reports, and executive-level presentations.
• Collaborate with cross-functional teams to support model development, implementation, and governance activities.
• Provide analytical guidance and subject matter expertise to stakeholders and business partners.
• Participate in global meetings and effectively manage interactions with multiple stakeholders.
• Execute ad hoc analyses and special studies to address business needs.
• Evaluate emerging technologies, tools, and methodologies to enhance analytical capabilities.
• Manage priorities and deliver projects within established timelines and quality standards.
  

Required

• Master's degree in Finance, Financial Engineering, Mathematics, Statistics, Economics, Data Science, Computer Science, Engineering, Operations Research, Analytics, or a related quantitative discipline.
• Strong understanding of the credit risk lifecycle and risk measurement methodologies.
• Hands-on experience in Probability of Default (PD), Loss Given Default (LGD), and Exposure at Default (EAD) modelling.
• Strong knowledge of predictive modelling and statistical analysis techniques.
• Experience applying Artificial Intelligence (AI) and Machine Learning (ML) methods in business environments.
• Proficiency in Python, SQL, and Google Cloud Platform (GCP).
• Experience with statistical techniques including:
• Linear Regression
• Logistic Regression
• ANOVA/ANCOVA
• Decision Trees (CHAID/CART)
• Cluster Analysis
• Strong analytical problem-solving skills.
• Excellent written, verbal, and presentation skills.
• Ability to communicate complex analytical concepts clearly to diverse audiences.
• Strong collaboration and stakeholder management skills.
  

Preferred

• Experience with SAS and R.
• Experience developing and implementing cloud-based analytical solutions.
• Knowledge of model governance, validation, and regulatory requirements within banking or financial services.
• Exposure to market risk and broader risk management frameworks.