Acuity Knowledge Partners - Associate/Senior Associate - Model Validation - CCAR/CECL (2-5 yrs)

CCAR, CECL - Model Validation (Assocaite/ Senior Assocaite)

Key Responsibilities:

- 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.

Job Requirements/ Qualification:

- M.Sc/PhD in Mathematics / Statistics / Economics or MBA Finance 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.

- Research mindset, strong analytical and problem-solving skills.

- Strong documentation skills.