Risk Modelling & Data Scientist Specialist

About this role

Primary Objective:

Support development and the monitoring of the industry leading regulatory compliant credit risk models, also internal models for early warning as well as behavioral models for Asset Liability Management (ALM) for effective risk management and decision making in the Bank on the designated subject area.

Key Responsibilities

• Support Head, Retail Risk Modeling in relation to the respective area’s risk modeling activities.
• Technical Expertise:
• Assist to develop and maintain industry leading credit risk models in accordance to Basel and MFRS requirements.
• Assist to develop Asset Liability Management (ALM) behavioral model to identify the behavior of deposits and loans for application in Interest Rate Risk in the Banking Book (IRRBB)/Rate of Return Risk in the Banking Book (RORBB).
• Assist to develop early warning model which predicts the likelihood of a customer moving from performing to arrears.
• Assist in preparing clear and thorough model development documentation for regulatory submission and approval.
• Assist in preparing model performance monitoring report for timely reporting to the management committee, the monitoring report served to provide insights to ensure correct use of model and enable critical decision making related to model recalibration or refinement.
• Prepare test script, test cases and perform User Acceptance Test to ensure model is implemented as per expected.
• Socialize with stakeholder including internal and business expert to gather business insight in the respective area of model development and data analytics.as well as to ensure the model is implemented as per expected.

Requirements

• Bachelor Degree
• Statistics, Mathematics, Actuarial Science, Economics, Finance, Financial Mathematics, Data Science.

- Cfa, Frm

• Minimum 1-2 years experiences or fresh graduate in good relevant qualifications.
• Good understanding on the respective area’s business products and operations.
• Good analytical skills.
• Good statistical modeling knowledge.
• Good understanding of relational databases and data models.