Model Validation Specialist

Team Description : Model Risk & Validation is responsible for the independent review and challenge of the models used within Investec Bank plc and helps ensure we manage Model Risk in line with policy, regulation, risk appetite and UK best practice. Key Responsibilities: As a Model Validation Specialist, your core responsibility will be the review and technical validation of the models used within Credit Risk, to help ensure the bank's models are managed in line with policy, regulation and risk appetite. Key activities include: Credit Model Validations: Conduct end-to-end independent model validations for our new and existing credit risk models (PD, EAD and LGD) for IRB and IFRS 9, in line with the bank's internal policies and standards and relevant UK regulatory requirements. Findings Management: Regularly track open validation findings and ensure they remain up to date and accurate and review any submitted evidence to support their closure, to facilitate the periodic model review process. Analysis & Reporting : Producing qualitive and quantitative analysis, which may include industry and statistical insights to support the model approval process and for senior management reporting. Model Risk Management: Carry out Model Risk BAU management activities, such as reviewing the bank's Model Inventory, supporting the development of the bank's Model Risk Framework, including policies and standards. Stakeholder Engagement: Work closely with Model Development, Internal Audit and other risk areas, as well as support the Credit and Business functions in their understanding of the models and their associated assumptions and limitations. Best Practice: Research, design and implement best practice methodologies and tools for validating credit risk models, in line with regulatory expectations (including the PRA Rulebook, supervisory requirements, SS1/23) and current industry practices. Core Skills & Knowledge : Modelling experience: Considerable experience in model development or model validation of, at a minimum, PD, EAD and LGD models for IRB and/or IFRS 9 across retail and/or corporate portfolios. Must have a strong understanding of common statistical modelling techniques and their associated assumptions and limitations. Educational background: Highly analytical with a degree in highly numerate field (such as Mathematics, Statistics, Econometrics, etc.) with strong academic records and understanding of mathematical and statistical theory. Analytical acumen: Exhibit strong analytical abilities, with experience in interpreting and summarising complex data, performing relevant statistical tests and presenting findings clearly to diverse audiences. Communication and documentation: Excellent written and verbal communication skills. Ability to articulate complex technical concepts to non-technical audiences in a clear and concise manner. Can prepare high-quality technical validation documentation as well as summarise key points for senior stakeholders. Coding proficiency : Demonstrable proficiency of coding in the Python programming language and supporting environments (e.g. Visual Studio). Collaboration and Prioritisation: Ability to build strong working relationships and work collaboratively with different stakeholders on various projects across the bank and act as an SME for the Validation function. Ability to effectively prioritise and manage multiple projects and/or deadlines and be adaptable to changing priorities. Continuous Learning: Display strong interest in mastering credit risk model validation and Model Risk Management and has a strong understanding of the UK's IRB regulatory environment, with an ability to keep to date with regulatory changes. Other good to have skills : · Experience with various modelling and validation approaches for low default and/or low loss portfolios. · Technical knowledge of relevant MRM regulation, such as PRA's SS1/23 and FRB's SR11-7. · Experience with Stress Testing and/or Forecasting model development or validation and having a general understanding of the ICAAP process. · Experience with model development and/or validation of non-credit risk specific model types, such as Operational Risk, AI/ML, CCR. · Experience with alternative programming languages such as R, MATLAB, SAS or SQL. - Experience of working in a fast-paced environment, including taking initiative to problem-solve, champion positive behaviours and build out capabilities.