Review and validate new and existing model and framework, provide effective challenge, ensure validation work quality.
Help manage model risk across the model lifecycle including model validation, performance evaluation and annual model reviews.
Challenge and continually improve MRM Guidance on modeling approaches and model performance testing.
Contribute to strategic, cross-functional initiatives within MRM organization.
Minimum of Master’s degree in a quantitative field (Statistics, Mathematics, Physics, Engineering, Computer science, etc.)
Higher academic qualifications and / or certifications such as a PhD, a second Master’s degree, CPA or CFA is a plus
Must have a strong background in statistical modelling techniques.
Good understanding of Model Risk Management.
Programming skills in using one or more of programming languages, such as Python, SAS, SQL, R, MATLAB, C / C++, Java, Oracle, etc.
Strong written and oral communication skills.
Teamwork and commitment a must as well ability to work independently.
Knowledge and understanding of a variety of model development and validation testing techniques covering risk models, including but not limited to linear regression models, logistic regression, generalized additive models, decision and regression trees, information gain and related segmentation statistical tools.
Previous familiarity with Risk models such as Credit Risk, Operational Risk, Liquidity Risk, Structured Products, Securities and Securitization (including AFS / HTM), Pension Models, Insurance Models, Interest Rate Models, Scenario Variables / Macroeconomic Forecasting models, Climate Risk, etc. is preferred.
Knowledge of financial instruments, pricing models, simulation and risk estimation methodologies, and regulatory requirements (prior knowledge of trading book products is a plus)
Derivative pricing skills (Risk neutral pricing, stochastic calculus, numerical techniques (finite differences, MonteCarlo simulation, binomial / Trinomial Trees, Numerical integration)), coding in C++ / python).