Build trust through transparency, reliability, and follow-through12+ years of experience in Finance, Accounting, and/or Internal Controls Demonstrated understanding of Sarbanes-Oxley (SOX) compliance requirements, including IT general controls (ITGCs), application controls, and COSO framework Demonstrated experience with program management of enterprise-wide initiatives, including governance, controls, or compliance frameworks Proven track record of operating in cross-functional environments and influencing stakeholders without direct authority Deep understanding of AI tools and a demonstrated experience showing willingness to continue learning Experience navigating ambiguity and building solutions in a rapidly evolving domain Bachelor's degree in Accounting, Finance, Business, or a related field Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements) Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews) Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies CPA, CIA, or CISA Experience implementing or managing AI/technology governance frameworks within a finance or accounting context Working knowledge of AI/ML concepts and hands-on experience with AI tools Experience in a large-scale, global technology company Accounting & Controls Expertise: Demonstrated understanding of financial controls, SOX compliance, and the intersection of accounting processes with technology - enabling you to serve as a credible advisor to both Finance and Engineering stakeholders Program Management: Demonstrated experience managing complex, multi-workstream programs with clear milestones, dependencies, and accountability structures across many different stakeholder groups AI Fluency & Innovation state of mind: Experience of learning AI, thinking through tough problems, and building solutions with a variety of tools as well as a track record of adopting and championing new technologies. This is a rapidly evolving space, and the framework must evolve with it System Requirements Definition: Partner with Engineering and Enterprise Products teams to define the technical requirements for the AI governance platform, including workflows, approval processes, risk classification, and monitoring features Platform Build & Optimization: Collaborate with engineering teams to build out and continuously enhance the governance system - ensuring it is AI first in how it operates, increasing automation, enabling user self-service, scalability and robust reporting Continuous Monitoring Features: Collaborate with core finance functions and EP to develop a robust continuous monitoring framework, starting with our highest risk areas and ultimately looking to establish a broad-based AI powered continuous monitoring technological framework that significantly enhances our control environment Technology Integration: Ensure the governance system integrates effectively with Meta's broader technology ecosystem and supports the tools and platforms Finance teams use daily Program Health Reporting: Establish and maintain comprehensive reporting for the AI governance program, including health metrics, compliance levels, risk indicators, and adoption dashboards Automated Reporting: Design and implement automated reporting features at multiple levels (team, function, leadership), reducing manual effort and increasing timeliness and accuracy of governance insights Data-Informed Decision Making: Leverage program data to identify trends, surface risks proactively, and inform continuous improvement of governance policies and processes Stakeholder Engagement: Engage with Finance functions and cross-functional teams to ensure they understand AI governance requirements, their responsibilities, and the resources available to them Enablement Materials & Training: Develop and deliver (or enable) training programs, toolkits, and reference materials that empower teams to govern AI effectively within their domains.