Expert, Credit Risk Analytics and Modelling

Job Purpose
The job holder responsible for:
-Research advanced techniques applied in market risk and liquidity risk measurement, analytics and monitoring
-Research into national and international regulations and standards in market risk, liquidity risk and interest rate risk in the banking book, and provide practical solutions
-Advise solution in quantitative model development applied in market risk, liquidity risk, interest rate risk in the banking book and pre-settlemen risk for seniors in higher levels.
-Research technical application in market risk, liquidity risk, IRRBB and pre-settlement risk management

Key Accountabilities:
1. Data Solutioning for Credit Risk Management
- Build cutting-edge algorithms and work with machine learning and deep learning tools to deliver advance analytics solutions to support credit decision making
- Implement and monitor credit risk models in compliance with regulatory requirement and industry practices (SBV, Basel, IFRS9)
- Drive application of machine learning and big data techniques with decision strategy across customer credit life-cycle journey: underwriting & approval, early-warning, collection & recovery
- Evaluation of effectiveness of proposed models and track business performance (risk vs reward) against data analysis model.
- Manage, execute, and review complex risk-related data science projects in an agile manner, and in compliance with internal & regulatory requirements.

2. Credit Risk Analytics & Data Insighting
- Prepare credit risk management reports & insights to senior leaders and relevant units internal/external division/bank.
- Support in the assessment of portfolio and in-depth analysis by forums and boards: BOM/BOD; ARCO/ALCO; other as requested.
- Perform portfolio credit quality forecast, scenario analysis/simulation for management decision / strategic planning / ICAAP, etc…

3. Credit Risk Analytics Infrastrucutre, Research & Development
- Research, compare, apply external/alternative data sources and advanced machine learning/ deep learning techniques into risk model development as benchmarking/challenger model for continuous improvement and upskilling
- Operate and maintain credit risk feature stores and model deployment platform (batch-run model and real-time decision engine),
- Operate and maintain credit-risk related datamart and information system (debt classification, provisioning, and credit risk metrics) in accordance with regulatory and international standards (Basel II, IFRS9)

4. Projects Management
- Minimize project conflicts, challenges and dynamic business requirements to keep operations running at high performance.
- Collaborate with team Leads to resolve people problems and project roadblocks, conduct post mortem and root cause analysis to help squads continuously improve their practices to ensure maximum productivity.