Senior Director – Internal Audit - Data Science and AI

emergiTEL•Washington DC, DC

About The Position

In this senior leadership position in Audit, the Senior Director leads the AI, Product, and Data Science vision and advances Audit’s AI strategy, analytics, automation, and enterprise technology delivery. The role builds and scales technology solutions that improve audit and enterprise risk management quality, efficiency, and insight generation while maintaining strong governance of analytics and technology enablers. The Senior Director partners across Audit, Technology, Enterprise AI, data science, and risk organizations to architect reusable products on a unified platform. These capabilities strengthen risk detection, continuous monitoring, evidence generation, and control-risk reporting. The role is accountable for building a high-performing team, delivering measurable efficiency and insight gains, and positioning Audit as an innovation leader across the enterprise.

Requirements

At least 8 years of relevant experience in data science, AI, analytics, automation, product management, technology delivery, audit technology, or related enterprise technology functions.
At least 5 years of leadership experience building, coaching, and leading high-performing, diverse teams of data science, AI, automation, product, and technology professionals.
Proven track record delivering enterprise AI, analytics, automation, or data products from strategy through production adoption, with measurable business outcomes.
Experience leading AI-enabled transformation in Internal Audit, risk, compliance, financial services, or another highly regulated enterprise environment.
Deep expertise in AI/ML, GenAI, automation, advanced analytics, data platforms, cloud technologies, model lifecycle management, and scalable software or product delivery practices.
Experience partnering with Technology to design and scale platforms, reusable capabilities or microservices, governed data products, APIs, and enterprise-grade AI capabilities.
Strong understanding of data governance, model risk, responsible AI, and regulatory expectations in financial services or similarly regulated environments.
Executive-level communication and influencing skills, including the ability to advise executives and VP+ stakeholders, challenge assumptions, and explain technical concepts to non-technical audiences.
Experience establishing strategies, operating models, talent plans, and performance measures that accelerate delivery, reuse, workforce adoption, and measurable efficiency gains.
Bachelor’s degree or equivalent experience in computer science, data science, statistics, engineering, information systems, business, or a related field.
Curiosity and adaptability in learning and responsibly applying new technologies, including artificial intelligence, to reimagine how work is done.

Nice To Haves

Advanced degree in computer science, data science, statistics, engineering, AI, analytics, business, finance, audit, risk, or a related field.
Strong understanding of Audit, controls, or risk management.
Experience with GenAI/LLMs, retrieval-augmented generation, intelligent agents, NLP, anomaly detection, machine learning, orchestrated multi-agentic workflows, or knowledge graphs.
Familiarity with cloud AI and data platforms, MLOps/LLMOps, model monitoring, data quality controls, secure software delivery, and enterprise architecture practices.
Relevant certifications or credentials, such as machine learning, cloud, or AI certifications, or equivalent professional experience.

Responsibilities

Lead the development and execution of Audit’s AI, analytics, and automation roadmap in alignment with Board goals, Audit priorities, and the enterprise AI vision.
Own the strategy, product roadmap, delivery, and adoption of AI, GenAI, analytics, and automation capabilities that generate measurable efficiency, quality, and insight gains across the audit lifecycle.
Partner with Technology to architect and scale a unified audit platform supporting reusable data assets, analytics, automation, GenAI services, model operations, secure delivery, and enterprise-grade controls.
Attract, develop, and retain high-performing data science, AI engineering, product, and automation professionals, and upskill the existing audit workforce.
Advise Audit executives and VP+ stakeholders on the value of AI and advanced analytics while managing data, model, operational, and ethical risks.
Shape a platform-based approach that reduces time to market, increases reuse, and enables AI-supported anomaly detection, full-population testing, evidence generation, actionable insights, and control-risk reporting.
Collaborate with Enterprise AI, data science, technology, risk, compliance, and business leaders to extend Audit-built capabilities across the three lines of defense and improve enterprise risk management effectiveness.
Enable continuous monitoring, stronger risk detection, automated evidence generation, and data-driven insights.