Director-Data Analytics
American Express’ Internal Audit Group (IAG) has reinvented our audit process and is leading the financial services industry with our Data-Driven Continuous Auditing methodology embedding intelligence through the audit lifecycle. Analytics & Insights team at IAG is uniquely positioned to support our department’s Winning Aspirations to be a world class internal audit function that is:
- Innovative - Leveraging data, automation, and AI throughout our processes
- Impactful - Delivering timely, pragmatic and forward-looking risk insights across the organization
- Influential - Developing leaders to enrich the colleague experience
Collectively, IAG’s strategic initiatives, combined with our greatest asset - our people - enable IAG to evolve through the design, development, deployment, and governance of AI-driven capabilities. These include custom GPTs, enterprise Copilot agents, agentic workflows, MCP-enabled integrations, advanced analytics, automation, and data-driven audit solutions that provide greater and more continuous assurance across the organization.
We are looking for those who share our mission and aspirations and are passionate about the use of data and technology in a collaborative, people-focused environment.
- Lead a team of analytics and AI developers responsible for designing, building, testing, deploying, and continuously improving AI-enabled audit capabilities, including custom GPTs, Copilot agents, agentic workflows, MCP-enabled integrations, advanced analytics, and automation solutions.
- Identify, prioritize, and lead opportunities to embed AI, analytics, and automation into audit planning, risk assessment, control testing, issue validation, reporting, and continuous auditing processes.
- Translate business risks, controls, audit objectives, and supporting data into analytic and AI solution requirements, partnering with developers and stakeholders to deliver effective analytics, agents, automation, and insights.
- Manage multiple, simultaneous audit analytics projects of varying size and complexity across business areas, regions, and audit teams, including unfamiliar or emerging risk areas.
- Analyze and interpret results from Analytics Control Tests, Data Driven Audit Procedures, advanced analytics, and AI-enabled outputs, helping audit teams generate actionable, forward-looking risk insights.
- Establish and maintain quality assurance, validation, monitoring, documentation, governance, and change control protocols for analytics, automation, and AI-enabled solutions.
- Build and maintain strong relationships with IAG Portfolio General Auditors, Audit Leaders, team leaders, technology and business partners, risk management and control function partners, and regulators.
- Communicate complex analytics and AI solution designs, results, risks, limitations, and business impact to non-technical senior audiences while championing AI literacy, responsible usage, training, and cross-skilling across IAG.
7+ years’ experience in public accounting, finance, internal audit, analytics, technology, risk management, or related fields, preferably within the Financial Services/Banking industry.
BA, BS, or equivalent degree in Accounting, Finance, Data Science, Computer Science, Information Systems, Engineering, or a related field; advanced degree preferred.
Demonstrated experience leading analytics, automation, or AI development teams from idea generation through production deployment and adoption.
Hands-on or leadership experience developing AI-enabled solutions such as custom GPTs, Microsoft Copilot agents, LLM-powered applications, AI assistants, workflow automation, or agentic solutions.
Experience translating business, audit, risk, and control requirements into technical solution designs, including data requirements, user workflows, controls, testing criteria, and success measures.
Understanding of LLM solution patterns, including prompt engineering, system instruction design, retrieval-augmented generation, knowledge grounding, tool/API integration, agent orchestration, evaluation, monitoring, and human-in-the-loop controls.
Familiarity with secure integration approaches for AI solutions, including APIs, enterprise data platforms, document repositories, workflow tools, and MCP servers or similar agent/tool connectivity frameworks.
Track record of redesigning business processes to be AI-enabled, automated, data-driven, measurable, and aligned with risk and control expectations.
Aptitude for working with data, interpreting results, business intelligence, analytics best practices, and the responsible use of AI-generated outputs.
Ability to lead development of analytical capabilities by leveraging advanced analytic methods such as machine learning, forecasting, cluster analysis, pattern matching, natural language processing, generative AI, and large language models.
Ability to break down complex problems into components, solve them using data analysis, process knowledge, AI-enabled methods, and risk/control knowledge, and communicate analysis, issues, limitations, and control recommendations with clarity and professionalism.
Ability to effectively integrate business, operational, technological, data, AI, and financial components into audit work.
Demonstrated understanding of AI governance, responsible AI, privacy, information security, access control, model risk, testing, documentation, and change management considerations.
Demonstrated track record of integrity, effective communication, innovation, and excellence.
Strong written and verbal communication skills that deliver high-quality, actionable, and value-added feedback to management on potential control issues and potential solutions to close gaps.
Proven ability to lead global team members in a way that encourages development, cross-skilling, innovation, accountability, and results.
Preferred Qualifications
Background in credit risk management highly preferred.
Experience with Microsoft Copilot Studio, Microsoft 365 Copilot extensibility, OpenAI APIs, custom GPT platforms, agent frameworks, or comparable generative AI development tools.
Experience with retrieval-augmented generation, semantic search, vector databases, embeddings, knowledge management platforms, or enterprise search.