- In this role you will rely on your strong quantitative skills to develop and implement models and analytical solutions for our clients that are mostly within the banking and insurance industries and more specifically within the credit and operational risk domains.
- You’ll use quantitative and qualitative analysis techniques and need to be familiar with international accounting standards, Basel II capital requirements, credit risk modelling, operational risk scenario and loss data modelling, rating criteria and stress-test modelling, on-going monitoring of rating models, creation of reports and dashboards, and communication of findings to clients.
- To ensure success in this role we need someone with solid communication skills who can confidently explain concepts to clients, conduct training sessions, and lead presentations. You also need to be able to collaborate, show strong leadership skills, and have the skills and experience to easily adapt to different environments and projects.
FURTHER RESPONSIBILITIES
- Development, implementation and optimisation of credit and operational risk models (Basel II regulatory capital models, impairments)
- Development and implementation of various quantitative models for clients in the banking and insurance industries
- Credit risk and operational risk evaluation, including qualitative and quantitative approaches, as well as modelling, analytics, and forecasting
- Data mining, scrubbing, cleaning, mapping and analysis using SAS, SQL or VBA skills
- Compilation of model build and business requirement documentation
- Creation of MIS dashboards
- Development and implementation of solutions to monitor the performance of existing models
- Innovative first-principles model building and automation of processes.
QUALIFICATIONS AND EXPERIENCE
- Completed Mathematical or related Degree
- 2 to 5 years’ quantitative analysis or modelling experience
- Competent in SAS, SQL or VBA
- Ideally a Credit or Operational Risk, risk background
- Banking exposure preferable
- Knowledge of building of fraud models
- Solvency II implementation
- Market Risk and Financial Modelling knowledge
- Data mining and analytics/statistics.
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