Job Requirements
Model Risk Management
Model risk relates to the risk of inaccurate assessment of underlying risks arising from inappropriate model development, calibration weaknesses or incorrect application of the model and includes the risk of under-estimation or over-estimation of risk. The models used by the Bank are being governed under Model Risk Management framework for model lifecycle. The model lifecycle involves working in all phases including development, validation, monitoring, issue identification, resolution, documentation, and governance.
Role involves working independently overseeing the management of model risk exposures across business lines covering Retail, Wholesale, Fraud, Market Risk, Counterparty Credit Risk, Liquidity Risk. The unit maintains oversight of all the models from implementation to usage to retirement.
Models under coverage
Retail Credit and Fraud Detection Models: Use statistical, econometric and machine learning techniques for variety of applications like lending scorecards, fraud detection, behavioural assessment.
Primary Responsibility
- Initial Sign off & Validation - Independently performing statistical and mathematical model development or validation for Retail Lending Scorecards, and ensuring the conceptual soundness of the models.
- Re-validation - Lead initiatives to improve the accuracy, availability, granularity and coverage of our existing model validation process and build tools for process automation. Extracted and analyzed data from various sources to support business intelligence and decision-making processes.
- Model Risk Dashboard - Assisted in the development and maintenance of a model monitoring dashboard, ensuring real-time tracking and performance evaluation of machine learning models.
- Code repository - Created standardized code for validation and monitoring exercises, ensuring consistency and efficiency for future use.
- Data Repository - Developed a comprehensive data source repository for all retail scorecards, facilitating easy access and management of critical data.
Technical Skills
- Programing skills and using tools such as Python, R, SAS and SQL or other programing language
- Database management skills - SQL, Hive, NoSQL or any other relational databases and data warehouse (SAS Viya, Power BI)
- Understanding of parallel and distributed computing framework like Hadoop/Spark/H2O
- Understanding of Statistics, Differential Mathematics, Probabilities, Machine Learning, Database Management, Big Data
- Experience with data visualization application (e.g. tableau) development using Python Dash or R Shiny is preferred
- Any cloud exposure like AWS/Azure/GCP is preferred.
Behavioural Skills
- Ability to work independently on complex problems from solution to implementation
- Eagerness to learn continuously and work on new domains / problems
- Ability to undertake research and come out with solution for problems
Experience
- 0 to 3 years of experience
- Master's in Statistics, Mathematics, Economics, Computer Science, Operation Research, Engineering, Physics
