The CLM Specialist provides deep subject matter expertise, to functional colleagues and internal stakeholders, on one or more specific functional subject area / regulation (e.
g. stress testing methodologies and scenario analysis, calculation of adjustments / reserves) for the effective management of capital and liquidity positions, in line with the Bank’s policies and regulatory requirements.
The CLM Specialist performs detailed analysis, highlighting capital and liquidity risks and recommends solutions to actively manage these risks.
They may also help develop and implement tactical solutions to optimise funding for the Bank, including management of the liquidity pool and associated risks.
The CLM Specialist is responsible for developing best practice approaches and building subject matter capability within the Function, in response to the changing regulatory landscape.
They will manage relationships with relevant stakeholders and collaborate with colleagues within / across functions to support the execution of the business strategy and achievement of the Bank / Division objectives.
Role Description - Summary
The primary function of this role is contribute to the bank’s balance sheet and income statement modelling methodologies in support of capital planning and other end uses, such as interest rate risk in the banking book, liquidity, and planning.
The suitable candidate will have the following responsibilities in this role :
related decisions pertaining to business line segmentation, data, model development decisions, and model choice criteria.
level information to a general understanding.
Responsibilities / Tasks
The position of Associate / Assistant Vice President Modeling & Analytics is responsible for (but not limited to) :
Experience / Exposure
The role requires strong quantitative skills, including : Knowledge and modeling experience in at least one area : PPNR, Credit, Market, ALM principles and relevant interdependenciesFamiliarity with time-
series, account level, and panel data analysisUnderstanding of linear and non-linear interdependence between risk factors such as variance-
covariance or copulasKnowledge of key distributions and their implementation in various analysis contextsProficiency in at least one programming language such as SAS, R, and MATLAB
Education / Qualifications
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