AVP, Finance - Predictive Analytics & Modelling MY

About the role

To lead the execution and delivery of predictive modelling, analytics, and decision management capabilities within assigned portfolios or business areas, supporting Group-wide decisioning frameworks. The role ensures robust, compliant, and data-driven model and decision outputs, aligned with regulatory standards (including IFRS 9), while driving continuous improvement, operational efficiency, and adoption of advanced analytics.

Key responsibilities

• Translate modelling and decision management strategies into practical implementation plans and drive execution of analytics and decisioning use cases within assigned portfolios
• Lead the development and implementation of credit risk and predictive models (PD, LGD, EAD, behavioural)
• Ensure models are fit-for-purpose, explainable, and compliant with regulatory requirements
• Oversee model monitoring, backtesting, recalibration, and performance reviews
• Manage configuration and enhancement of decision engine rules, scorecards, and segmentation logic
• Generate insights to support credit strategies, portfolio optimisation, and financial performance
• Ensure models and decision tools comply with internal policies and regulatory requirements (e.g., IFRS 9)
• Partner with Risk, Finance, Business, Tech, and Data teams to deliver solutions
• Manage and coach a team of analysts/modellers
• Review deliverables to ensure quality, accuracy, and compliance

About you

• Bachelor's Degree in Actuarial/Statistics/Finance or any relevant qualifications
• 8–12 years of experience in credit risk modelling (PD, LGD, EAD, IFRS 9) and financial analytics or data science in financial institutions
• Proven experience in end-to-end model lifecycle (development → monitoring → validation support) and stakeholder engagement across Risk, Finance, and Business
• 3–5 years in people or project leadership role (for Manager / AVP / VP track)
• Strong understanding of regulatory frameworks (IFRS 9, BNM/MAS guidelines) and banking products, portfolio management, and credit risk drivers
• Hands-on experience with analytical tools (SAS, Python, SQL) and reporting platforms
• Exposure to decision engines, scorecards, and automation frameworks
• Technical skills in accounting standards (IFRS/MFRS/GAAP), credit risk modelling, banking products and operations, financial and statistical modelling, and financial data analysis
• Non-technical skills in strategic planning, leadership capabilities, collaboration, prioritization, communication, change management, and conflict management