Market Risk Analyst
2 weeks ago
3 to 6 years
in
Market risk (Model Validation or Model Building).Gain deep understanding of the model inventory and the model risk management framework and lifecycle of the clientValidate CCAR Stress Testing for PPNR, balance sheet, scenario design and loss forecasting models used for internal risk management and provisioning as well as regulatory submissionsAssess the data requirements of the model, assumptions, and model methodology, analyze model outcomes, track ongoing model performance, and run stress tests on models based on company specific or regulatory stressed economic scenariosProvide guidance to model owners for remediation of issues identified during validation to achieve MRM policy adherenceProvide support to client’s model risk management group in meeting their regulatory commitmentsInteract proactively and build strong relationship with various model owners/stakeholdersRepresent client’s model risk management group in interactions with model owners across lines of businessDrive adoption of best practices for model validation, assessment and documentationAbility to work well in a team and feel comfortable presenting model validation findingsGain ongoing knowledge of accepted practices and current research in the areas of mathematical modelling in finance and model risk management, though academic literature and respected financial/economic journalsMentor junior quantitative analysts and conduct training sessions on mathematical modelling, quantitative analytics and risk management
Qualification:
M.Sc/PhD in Mathematics / Statistics / Economics or BE for Tier 1 collegeswith experience in an MRM roleExperience:
Either, candidate should have exposure to CCAR modelling in an applied settingFunctional CompetenciesWorking knowledge in Stochastic Calculus, Linear Algebra, Differential Equations, Statistics, Simulation, Computational Methods and Numerical Analysis (working knowledge or coursework in mathematical finance will be an advantage)Strong knowledge in mathematical techniques used in the development of VaR computation, Risk Charge calculations Tail Risk modellingWorking knowledge of Copula models, Monte Carlo simulation and techniques used in Traded Credit Risk modelling will be a huge plusWell versed in regression modelling (Multiple Linear, Logistic, Polynomial, Beta, Poisson, and Time Series) as well as regularization techniques used in regression such as Ridge/Lasso/Elastic Net etc.Working knowledge of machine learning models such as Decision Trees, Max Margin Classifiers and model ensemble ideas such as bagging, boosting and stackingWell versed in one of R/Python and MS Excel
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