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Market Risk Analyst
1 month ago
Key Responsibilities:
Minimum experience of 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 client
Validate CCAR Stress Testing for PPNR, balance sheet, scenario design and loss forecasting models used for internal risk management and provisioning as well as regulatory submissions
Assess 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 scenarios
Provide guidance to model owners for remediation of issues identified during validation to achieve MRM policy adherence
Provide support to client’s model risk management group in meeting their regulatory commitments
Interact proactively and build strong relationship with various model owners/stakeholders
Represent client’s model risk management group in interactions with model owners across lines of business
Drive adoption of best practices for model validation, assessment and documentation
Ability to work well in a team and feel comfortable presenting model validation findings
Gain 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 journals
Mentor 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 colleges
with experience in an MRM role
Experience: Either, candidate should have exposure to CCAR modelling in an applied setting
Functional Competencies
Working 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 modelling
Working knowledge of Copula models, Monte Carlo simulation and techniques used in Traded Credit Risk modelling will be a huge plus
Well 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 stacking
Well versed in one of R/Python and MS Excel
