About the role
The role is for an Executive Director/Senior Vice President in Technology Audit focusing on Data, AI, and Applications at a large bank. The position involves strategic audit leadership, audit execution, governance, team leadership, and stakeholder management. Candidates need 15+ years of IT audit or related experience with at least 5 years in second line of defence or audit in a financial institution.
BankingOnsite
Key Responsibilities
- Develop and champion a strategic vision for technology audit, specifically for Data, AI, and Applications, aligning with the bank's overall business strategy and risk appetite.
- Create and implement a comprehensive roadmap for achieving this vision, including key initiatives, resource allocation, and performance metrics.
- Lead the development of comprehensive audit plans considering the criticality of applications, data integrity, AI models, regulatory requirements, and business objectives.
- Proactively identify and assess emerging data, AI, and application-related risks, including those stemming from technological advancements, regulatory changes, and evolving ethical considerations.
- Develop and implement preventative strategies to mitigate these risks.
- Conduct and oversee comprehensive audits of IT applications, data management practices, and AI models, including evaluating system-related controls supporting IT infrastructure and applications.
Requirements
- Minimally 15 years of progressive experience in IT auditing, application support and development, application security, or risk management, with at least 5 years dedicated to Second Line of Defence or Audit within a large financial institution.
- Proven track record of successfully leading complex technology audit functions focusing on Data, AI, and Applications.
- Executive-level experience, preferably as a senior leader within an Audit or Risk function of a large, complex organization.
- Demonstrated experience in shaping and influencing strategic audit and risk direction is essential.
- Expertise in data risk, data governance, AI ethics, and model risk management, with a deep understanding of data and AI technologies.
- Familiarity with machine learning, deep learning, natural language processing, and model development lifecycles.