Product Risk & Compliance Audit Lead (AI-Driven)

Meta; Menlo Park (CA); Hybrid; USD 200,000 - 280,000; Full time

Job summary

Meta's Internal Audit team is seeking an experienced audit professional to lead product risk and compliance engagements across Meta's consumer and enterprise products. You will evaluate controls across product development lifecycles, privacy frameworks, and regulatory obligations, partnering with product, legal, privacy and compliance teams to drive improvements.

The role requires running AI-assisted audits, using data analytics and AI-enabled workflows to enhance coverage, testing efficiency

Qualifications

• 8+ years of audit, compliance, or risk management experience.
• Experience designing risk-based audit programs for product development, compliance, or data governance.

Responsibilities

• Lead product risk and compliance audit engagements across Meta’s product portfolio.
• Evaluate design and effectiveness of controls in product development, privacy, and regulatory areas.
• Partner with product, legal, privacy, and compliance teams to drive improvements.
• Lead AI-assisted audit engagements and leverage data analytics to improve coverage and efficiency.

Skills

Audit experience

Risk management

Data analytics

AI tools

Cross-functional collaboration

Executive communication

AI governance

Cybersecurity

Privacy frameworks

Regulatory compliance

AI in auditing

Education

Bachelor’s degree in MIS/Accounting/Business/CS/Engineering/Data Science

Tools

AI tools

Data analytics techniques

Job description

Meta's Internal Audit team is seeking an experienced audit professional to lead product risk and compliance engagements across Meta's consumer and enterprise products. You will evaluate controls across product development lifecycles, privacy frameworks, and regulatory obligations, partnering with product, legal, privacy and compliance teams to drive improvements.

The role requires running AI-assisted audits, using data analytics and AI-enabled workflows to enhance coverage, testing efficiency