Lead the development and optimization of Market & Liquidity Risk models (e.g., pricing, IRRBB, liquidity risk, stress testing, and VaR models), and design/refine risk metrics and modeling tools to comply with regulatory requirements (e.g., IRRBB, LCR, NSFR) and industry best practices.
Collaborate with project managers, market & liquidity risk managers and Risk Data teams to build and enhance the in-house MLRM system
Partner with local MLRM teams, Treasury and Business divisions to provide modeling solutions, guide local risk modeling initiatives and ensure consistent modeling approach across different countries.
Assist in developing and maintaining model-driven regional MLRM dashboards and management reports for senior management and risk committees, ensuring accuracy of model results and risk insights.
Establish and enhance stress testing modeling capabilities, including scenario design, model building, risk vulnerability analysis and mitigation recommendations based on model outcomes.
Assist in reviewing and updating local model risk management policies/procedures related to Market and Liquidity risk modeling, ensuring alignment with regional framework, regulatory expectations and model governance standards.
Support model risk reporting to senior management and model committee
Requirements
Bachelor's degree or above in quantitative fields (Mathematics, Statistics, Engineering, Financial Engineering), with a strong background in risk modeling or quantitative analysis.
At least 3 years of hands-on experience in market/liquidity risk modeling, model development/validation, quantitative risk analytics or IRRBB modeling within a bank/financial institution.
Familiar with MAS regulations and BCBS principles for market/liquidity risk (e.g., IRRBB, LCR, NSFR, stress testing), with deep understanding of model governance and validation requirements.
Strong quantitative modeling skills, with experience in developing, calibrating and validating risk models (e.g., VaR, IRRBB) and interpreting results for stakeholders.
Excellent communication skills, able to collaborate cross-team and convey complex modeling concepts to non-quantitative audiences.
Self-driven, independent, and able to prioritize multiple modeling tasks in a fast-paced environment to meet deadlines.
Proficiency in Python, SQL or VBA, with experience in quantitative modeling, data manipulation and model automation.
Plus: Experience in data analytics, dashboard tools (Power BI, Tableau) for model output visualization, or risk system implementation focusing on modeling modules.
