Expert, Quantitative Risk Modelling

Job Purpose
We are seeking for successful candidates to fill multiple positions (Senior Officer, Expert, Manager, Director) for this function who can interact well with business/risk/validation teams as well as a senior stakeholders in Risk, Finance and other functions and throughout TCB.The candidate is a specialist who applies mathematical and statistical methods to financial and risk management problems. She/he can develop and implement complex models used by the organization to make financial and business decisions about issues such as investments, pricing and strategic directions. In management position, this is a hands-on role, suited for an experienced professional who can combine deep technical expertise with a broader skillset of effectively manage teams and stakeholders to drive a strategic agenda.
Key Accountabilities:
1. Quantitative analysis to support decision making
• Research, study new technology, new solution to enhance model development quality
• Oversee and provide guidance and solution for the development during development process for technical problem solving
• Independent challenge developer about the selection of parameter, calibration methodology,...during model development and refinement
• Review/validation of upstream model of risk quantitative model are validated before using (that applies for MR, CCR, IRRBB and LR)
• Research and study international standard, best practice about model governance, model risk management, model development and validation to standardize quantitative modeling
• Model risk management activities in accordance with defined procedures
• Document and provide references for analytical methodologies, algorithms, models and related process,
• Train on the use of risk models
2. Risk data and System
• Research, study new technology, new solution to enhance model performance
• Design data architecture, database for MRM analytics and modeling dashboard
• Works with large datasets and complex algorithms to solve data science challenges.
• Leverages big data to develop innovative deployable solutions.
Help introduce best-in-class, cutting edge Model techniques to drive profitability.
3. Risk policies
• Involve to develop ad-hoc and regular summary reports and reporting dashboards to meet stakeholder needs
• Review reports for trend analysis, baseline benchmarking and modelling outputs
• Communicate findings of data modelling, monitoring and analysis
4. MLR transformation
• R&D of new technology/tool, new quantitative modeling and quantification approach, best industry practice can be applied for MR, LR, IRRBB, CCR as AI/ML, Fintech technology...to optimize model performance, risk analytics