The Model Risk & Validation Manager will be responsible for the risk measurement and diagnostics of all automations across Meta Business Services (MBS). Reporting into the MBS Risk team, this role will support our centralized monitoring and governance system that inventories, measures risk, and diagnoses the performance of automations across finance operations. The role requires a blend of risk measurement expertise, technical understanding of AI/ML systems, and cross-functional leadership experience, including aligning stakeholders across engineering, data science, and business operations to deliver shared outcomes to ensure automations are appropriately measured, diagnosed, and governed in line with company policies and regulatory requirements.
Responsibilities
Provide operational oversight of the oversight framework, ensuring it identifies, catalogues, and diagnoses all MBS automations Manage the human-in-the-loop approval process for newly identified automations, ensuring appropriate review and sign-off Drive continuous enhancement of existing real-time diagnostic and monitoring capabilities for production AI/ML integrations Design, implement, and own the risk measurement framework for AI and automated decision-making across MBS processes Develop quantitative and qualitative risk metrics to assess the impact, reliability, and exposure of each automation Conduct regular risk assessments of automations to ensure they operate as intended and do not introduce unintended bias, errors, or financial exposure Oversee the validation and testing of AI and decisioning models to ensure accuracy, reliability, and fairness Implement and maintain continuous monitoring processes with clearly defined diagnostic triggers and escalation thresholds Own the diagnostic capability for MBS automations — identifying root causes of performance deviations, control failures, or unexpected outcomes Define key performance and risk indicators (KPIs/KRIs) for automations and ensure regular reporting against these Work with data scientists and engineers to refine and improve models based on diagnostic findings and incident learnings Support maintaining compliance with the governance framework for MBS automations, ensuring consistent standards for risk measurement, diagnostics, and oversight Support the enforcement of policies on AI model usage, automated decisioning, and process automation risk measurement across all MBS business areas Support compliance with global regulations, internal Meta policies, and industry standards related to AI, automation and data usage Own and maintain the comprehensive automation inventory covering all business-critical process automations and AI/ML use cases across MBS Develop and maintain a change management process for automations Ensure appropriate oversight and documentation of all changes to automations Define an escalation path for changes and ensure leadership visibility Collaborate with data scientists, engineers, and business operations across MBS to ensure a cohesive approach to automation governance Partner with Revenue Operations, Credit, Collections, Procurement, and other MBS functions to ensure automation risk measurement scales appropriately Promote awareness and understanding of automation risk measurement across MBS and Finance Maintain comprehensive documentation of risk measurement methodologies, diagnostic processes, the automation inventory, and compliance activities Prepare and present reports to senior management and relevant stakeholders on the risk posture, diagnostic outcomes, and overall health of MBS automations Ensure transparency in AI and automation usage through clear, accessible, and regular reporting Stay current on developments in AI governance, model risk management, and regulatory requirements, ensuring MBS adapts accordingly Build risk measurement and diagnostic capability within the MBS Risk team and across partner functions
Qualifications
8+ years of experience in model risk management, model validation, or quantitative risk within financial services, fintech, or a large-scale technology company Experience independently validating quantitative financial models, including statistical, machine learning, or simulation-based models used in forecasting, treasury, or risk management Experience designing or operating model risk governance frameworks, including model inventory management, risk tiering, and validation lifecycle processes Experience communicating complex quantitative findings in writing to technical and non-technical stakeholders, including finance leadership Experience identifying control gaps in model risk programs and driving cross-functional remediation initiatives Experience validating machine learning or AI-based models used in financial decision-making, including fairness, explainability, and stability assessments Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies Experience with regulatory model risk guidance (e.g., SR 11-7 or equivalent frameworks) and applying those principles in a non-bank or technology company context Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews) Proficiency in Python, R, or SQL for model performance analysis, data validation, and automation of monitoring workflows Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements) Professional certification in financial risk management (e.g., FRM, CFA, or equivalent quantitative credential)