• 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.