Business QA - Data Analytics

In this role, you are expected to contribute to Institutional Credit Management's (ICM) objective of providing comprehensive credit underwriting, identification, measurement, management, monitoring, and reporting for wholesale credit businesses. As part of the ICM In-Business Quality Assurance function, you will play a crucial role in verifying that established standards and processes are consistently applied. Your insights from quality assurance reviews will help assess the group's policies, procedures, programs, and practices related to wholesale credit risk management, enabling the identification of operational weaknesses, training needs, and process deficiencies. As the ICM In-Business Quality Assurance Data Analytics Vice President, your responsibilities include executing Quality Assurance Reviews (QARs), providing feedback on deficiencies and areas of improvement, and engaging with management, Independent Risk, and other stakeholders. You will report to the ICM In-Business Quality Assurance Data Lead and should ideally have experience in Business Audit or Risk Management, coupled with expertise in Machine Learning, Data Science, and Analytics within the context of Wholesale Credit Risk processes in global financial services firms. Key responsibilities of this role involve supporting the Head of Data Analytics in setting the global strategy for a robust data analytics and testing program, challenging the design and operation of data and credit processes, analyzing complex datasets, deriving insights from data, evaluating findings, assessing data accuracy, monitoring model performance, staying abreast of regulatory changes, and providing guidance on data-related issues. You will act as a subject matter expert for senior stakeholders, exercise independent judgment, and operate with a limited level of direct supervision. To be successful in this role, you should possess strong analytical skills, communication abilities, knowledge of Wholesale Credit Risk, leadership qualities, organizational skills, problem-solving capabilities, attention to detail, a drive for learning new technologies, and experience with programming languages and data analysis tools. A degree in a quantitative field and proficiency in tools such as SQL, Python, SAS, R, Tableau, and Power BI are preferred qualifications. If you are seeking a challenging role where you can contribute to enhancing data analytics within the context of Wholesale Credit Risk and collaborate with various stakeholders to drive improvements, this position may be an ideal fit for you.,