Lead, Credit Risk Model 

We are currently looking for an experienced credit risk professional to join our Risk team to cover ECL model refinement, income model, analytic activities and perform stress tests. You will be responsible to develop credit risk model, IFRS9 and Pillar 2 stress testing model for measurement for the retail portfolio. 

  • Develop credit risk model, IFRS9 and Pillar 2 Stress testing model for measurement for the Retail portfolio.
  • Support continues improvement efforts through research on techniques process and domain. 
  • Execute end to end model development steps. The responsibility will also include efforts on data preparation, documentation, presentation and maintaining minutes of key decision made during the development lifecycle.
  • Adopting best coding standards and automation to help create coding repositories for various methods used across modelling team.
  • Ensure that models are fit for purposes not only for regulatory estimate but also for daily business usage, underwriting decisions, risk appetite decision and strategy design.
  • Participate in relevant model implementation and its user acceptance test ensure models are appropriately implemented not only within the direct system environment but also its relevant downstream environments.
  • Understand Model related uncertainty risk such as data, regulatory, business strategy that have a direct impact on the model’s performance.
  • Conduct stress tests as required by internal and external factor / stakeholders, including local regulators and review results and assess their implications as well as plan the remediations.
  • Obtain assurance regarding the effectiveness of credit controls and compliance with applicable laws & local regulation.
  • Ensure the modelling process and models meet the Model Policy and standards. 
  • Degree in Quantitative Discipline (eg. Mathematics, Statistics, Economics, Financial Engineering, Engineering).
  • Knowledge of Regulatory and Risk management (eg. Basel, IFRS9, Stress Testing, ECL etc).
  • Experience in Machine Learning analytics and/ or Retail Lending Risk Analytics will be an advantage.
  • Solid model experience.
  • Software skills: Python.
  • Strong communication skills.