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We have an exciting opportunity for an Assistant Manager to join our Credit Risk Assurance team. We are a specialist function within KPMG’s Audit Practice whose purpose it is to review and challenge complex statistical models used within audited entities as part of the overall financial audit process. Our team is made up of credit risk analysts from a broad and diverse range of quantitative backgrounds, including mathematics, engineering, physics, econometrics, and statistics. 

As a Credit Risk Modelling Assistant Manager, you will have a technical specialist role supporting the external audit of credit risk models (IFRS9, CECL, etc.). Your specific responsibility will be to execute and document the quantitative testing conducted by the team.

The role offers exposure to a wide range of techniques used by institutions, from globally systemically important banks to small credit providers. It also provides access to the latest technology, including cloud-based data processing and software packages like Python, R and SAS.

Roles and Responsibilities:

•Organizing and executing workstreams of quantitative testing. •Conducting assessments of audited entity model documentation (e.g. model development, validation, monitoring documentation) against the requirements of the accounting standard as well as industry practice.; •Annotating code produced by an audited entity and reconciling code functionality / variables back to documentation.; •Reperforming the output of an audited entity’s code by rerunning it in the appropriate software package.; •Reperforming the output of an audited entity’s code using code developed independently from an entity’s documentation using an appropriate software package.; •Determining independent estimates of a model’s output using KPMG assumptions. •Completing associated documentation for quantitative testing) in a clear and succinct manner. •Supervising, coaching and training junior team members on engagements.

Mandatory  technical & functional skills

Our Global Audit & Assurance Technical core competencies provide clarity and consistency of expectations to ensure the minimum audit technical requirements are being met by level to drive Audit Quality, which is fundamental to achieving our ambition to become the most trusted and trustworthy firm. The relevant competencies for this role are:

•Technical Knowledge:  Understands relevant technical accounting and financial reporting standards as well as being well versed in contemporary statistical techniques and practices in credit risk modelling. •Technology Skills:  Able to read, interpret and create software code, and is well versed in modern computing languages related to credit risk modelling (e.g. SAS, Python, or R). •Professional skepticism and issue identification:  Applies professional skepticism, objectivity and independence to identify and support resolution of potential audit issues. •Documentation: Completes  audit documentation demonstrating an unwavering focus on audit quality. •Detailed understanding of IFRS 9 and other relevant accounting standards is essential. •Detailed understanding of credit risk models and statistical estimation processes is essential. •Ability to read, understand and develop complex code in languages like R, Python, SAS is essential. •Proven track record of managing and delivering small workstreams (e.g., managing the delivery of a review of a non-complex model) is essential. •Experience of managing teams, coaching and mentoring junior staff is desirable.

Work Experience

•3-5 Year experience in relevant field.

Educational Qualification

•Degree in a quantitative subject is desired. •Excellent written and verbal communication skills; able to communicate complex matters in a clear and compelling manner. •Strong organisational and time management skills; able to work effectively in a fast-paced environment with conflicting priorities and deadlines. •Self-driven and resilient; able to thrive in a high-pressure environment. •Strong analytical skills and attention to detail; able to analyse and interpret complex technical worktypes.