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To perform pre & post-implementation validation on credit risk / IFRS9 models, in line with best practices.
To demonstrate in-depth understanding of the model risk management function and/or key business areas supported. Identify sources of model risk by thoroughly and comprehensively review all model components and developmental evidence.
To employ statistical modelling methodologies in performing validation and produce outcomes to be analysed from statistical as well as business perspective.
To keep abreast with market and regulatory changes. To be proficient in Regulators' IRBA requirements and ensure adherence.
To build strong working relationship with key model stakeholders,in particular model developers; and provide feedback and value added advice on the models. To provide healthy challenge to model developers and ensure models approved are of highest standard.
To clearly and concisely communicate the model review results to the rest of the team, other functions and senior management.
To present and explain the validation results to the management committee for endorsement and approval.
Take ownership of some of the team's internal initiatives and projects; and contribute to the various bank-wide projects that require quantitative technical expertise.
At least a Degree (or its equivalent) preferably in in quantitative discipline (Physics,Statistics, Mathematics,Finance, Economics, Engineering etc.) from recognized universities. Candidates with related post-graduate degrees (Masters or PhD.) have added advantage.
Relevant experience in credit risk / IFRS9 model development / validation or similar functions.
The candidate must be familiar with statistical modelling techniques and IRB credit risk models, such as Probability of Default (PD), Exposure at Default (EAD), Loss Given Default (LGD) models, as well as Application and Behaviour scorecards. Candidates with IFRS9 Expected Credit Loss (ECL) experiences have added advantage.
Proficiency in VBA / Excel / SAS is a must. Experienced in data analytics and data manipulations using SAS software. Familiar with various data base structures and able to work with IT on data extraction. Proficiency in machine learning programming in Python and / or R is preferred.
Self-motivated person with a high level of drive, dedication and desire to excel consistently.
Excellent verbal and written communication and interpersonal skills.
Meticulous, organised and self-assured with ability to interact well with various working levels.
A team player who is able to work independently under tight deadline and pressure.