Senior Analyst - Decision Sciences

We are currently seeking an experienced professional to join our team in the role of Senior Analyst - Decision Sciences Principal responsibilities Undertake model validation activities as dictated by the Global Model Risk Policy including the assessment of; model inputs, calculations, reporting outputs, conceptual soundness of the underlying theory and the suitability of the use for its intended purpose, relevance and completeness of data, qualitative information and judgements, documentation, and implementation of the model. Provide written reports detailing the results of validations highlighting issues identified during the validation. Validate remediation activities completed by the ILOD to ensure appropriate resolution of identified issues. Communicate technical model related information and results to Model Owners and Model Users through the course of a validation. Contribute to management, regulatory, and external confidence in all models used across the group. Deliver, high quality, timely validation reports that add value to the business. Requirements Candidate should have worked in development or model validation pertaining to Asset Liability Management models (Liquidity and IRRBB) including but not limited to Net Interest Income (NII) modelling, Economic Value of Equity (EVE) modelling, Prepayment modelling, NMD modelling, Cash flow forecasting of various asset classes, LCR/NSFR computation etc. Understanding of IRRBB - Gap/Optionality/Credit spread/Basis risk. Reviewed Pricing Models- Derivatives/ Product Control and hedging models, Variation/Initial Margin modelling, Structural liability forecasting, multi-curve construction, SOFR/OIS discounting and Value-at-Risk measurements. Should have the foundational understanding of pipeline, early redemption risk, prepayment, and extension risk. Hands-on experience with vendor systems such as QRM, PolyPaths, Murex, Bloomberg etc. Understanding of various stress testing models such as CCAR/PRA and various other mandatory regulatory expectations such as SR 11-7, SS 1/23. Must have background in financial mathematics knowledge such as stochastic calculus, numerical methods, probability theory, regression, econometrics. Foundational understanding of Machine learning techniques is desirable Minimum 1-5 years of experience of model validation/development experience in Risk Management in Treasury- Liquidity space. Experience with some statistical modelling software / programming languages e.g. Python, R, Matlab, C++, VBA. Experience of conducting independent model reviews. Ability to present complex statistical concepts and results to non-technical audiences in a persuasive and compelling manner. Team-oriented mentality combined with ability to complete tasks independently to a high-quality standard. Master s or PhD degree in a quantitative discipline like Financial Mathematics, Statistics, Econometrics, Quantitative Finance, Economics or Engineering. Professional certifications such as CQF, CFA, FRM will be considered a plus.