Internal Audit, Head of Innovation, NY

Goldman Sachs•New York, NY

48d•Onsite

About The Position

The Head of Internal Audit Innovation Strategy will lead the design, delivery, and institutionalization of cutting-edge technology, artificial intelligence (AI), advanced analytics, and automated testing across the global Internal Audit department. This individual will sit at the intersection of internal audit, data science, engineering, and digital transformation. The VP will lead a team of analytics and innovation specialists to re-imagine traditional audit approaches, drive continuous risk assessment, scale automated assurance solutions, and prepare the department for AI-enabled operational models.

Requirements

8–12+ years of progressive leadership experience in Internal Audit, Technology Audit, Risk Analytics, or Technology Consulting, ideally within Financial Services or Investment Banking.
Bachelor’s or Master’s degree in Computer Science, Data Science, Information Systems, Finance, Accounting, or a related quantitative field.
Hands-on experience or strategic oversight of Generative AI, LLM prompting/fine-tuning, Agentic workflows, and machine learning/predictive modeling.
Mastery in SQL, Python/R, data visualization platforms (Tableau/Power BI), and data preparation tools (e.g., Alteryx, Snowflake).
Deep understanding of internal audit methodologies, COSO, risk assessment, and regulatory expectations in financial services.
Proven track record of leading matrixed digital transformation projects, managing data teams, and presenting to senior executive committees.

Nice To Haves

CISA, CIA, ACCA/CPA, or relevant data/AI technical certifications strongly preferred.

Responsibilities

Define and execute a multi-year digital strategy for Internal Audit, focusing on Generative AI, Agentic AI, Large Language Models (LLMs), machine learning, and Process Automation (RPA) to automate repetitive audit tasks and scale risk detection.
Identify, prioritize, and manage high-ROI audit innovation use cases across the full audit lifecycle (planning, risk assessment, field testing, continuous monitoring, and reporting).
Establish robust methodologies and control frameworks to audit organizational AI systems, algorithms, and data governance frameworks for bias, drift, and regulatory compliance.
Oversee the development and maintenance of scalable data analytics products, interactive dashboards, and automated continuous risk monitoring tools (leveraging SQL, Python, Alteryx, Tableau/PowerBI, and enterprise data platforms).
Partner directly with business, technology, and capital markets audit execution teams to embed risk analytics and continuous auditing techniques into standard audit plans.
Direct delivery teams (data scientists, data engineers, automation architects, and business analysts) in maintaining data pipelines, integrating disparate legacy architectures, and ensuring data quality across audit tools.
Design and deploy comprehensive enablement programs (e.g., custom AI assistant adoption, Copilot training, low-code/no-code analytics workshops) to upskill non-technical auditors and achieve high adoption rates across the department.
Champion a forward-thinking audit culture that embraces curiosity, automation, and agile delivery principles.
Communicate technical concepts, innovation roadmaps, and business impact clearly to the Audit Committee, Executive Management, business line leaders, and external regulators.
Recruit, mentor, and manage a high-performing team of data analytics and technology innovation specialists across global hubs.