Data Defense Technical Project Manager

The Vanguard Group•Charlotte, PA

16h•Hybrid

About The Position

Drive forward Vanguard’s strategy to scale AI responsibly across the organization. In this role you will be responsible for leading cross-divisional efforts to operationalize Vanguard’s enterprise Continuous Monitoring function as a core component of the Responsible AI Framework. This role drives the execution, scale, and adoption of monitoring capabilities across AI systems, ensuring consistent observability, risk detection, and lifecycle feedback across all production environments. Operating in a highly federated governance model, the Project Manager coordinates across CDAO, IT, Risk, Legal, Product, and Engineering teams to establish standardized monitoring processes, align tooling (e.g., Arize), and operationalize monitoring requirements as outline in the AI governance Policy. The role focuses on closing cross-team execution gaps, ensuring traceability and audit readiness, and enabling scalable, risk-based monitoring practices across all AI systems. This role does not own AI risk decisions but ensures the Continuous Monitoring function is executed consistently, transparently, and in alignment with enterprise Responsible AI requirements.

Requirements

Bachelor’s degree or equivalent experience
5–8+ years of project/program management in technology, risk, or governance environments
Experience leading cross-functional initiatives in complex, regulated organizations
Familiarity with ITIL processes (incident, change, problem management)

Nice To Haves

Experience driving operationalization of enterprise platforms or controls
Experience with AI monitoring/observability platforms (e.g., Arize, WhyLabs)
Knowledge of Responsible AI, model risk management, or data governance frameworks
Exposure to risk-based control frameworks and enterprise governance models
Strong familiarity with AI/ML lifecycle, monitoring concepts, or data governance frameworks

Responsibilities

Cross-Divisional Program Leadership: Lead enterprise-wide rollout of Continuous Monitoring capabilities across AI systems.
Coordinate execution across CDAO, GTO, Risk, Legal, and divisional technology teams.
Drive alignment on monitoring standards, onboarding requirements, and operating model.
Monitoring Function Operationalization: Define and implement the enterprise Continuous Monitoring operating model.
Ensure integration with enterprise incident management tools (e.g., ServiceNow, PagerDuty).
Governance & Lifecycle Integration: Embed monitoring requirements into intake, assessment, and approval processes.
Support material change workflows and recertification processes driven by monitoring outputs.
Risk-Based Monitoring Framework Execution: Drive adoption of standardized metrics, thresholds, and “judges” across AI systems.
Ensure risk-tiered monitoring practices are consistently implemented across divisions.
Incident Detection, Response, and Continuous Improvement: Enable automated alerting, escalation, and triage workflows for monitoring events.
Integrate with current processes around post-incident reviews and root cause analysis.
Drive continuous improvement by feeding insights back into upstream lifecycle phases (i.e. pre prod monitoring on VG Bench).
Reporting & Executive Visibility: Deliver enterprise-level dashboards and reporting on system health, incidents, and compliance.
Prepare executive-ready materials for governance forums and senior leadership.
Establish a single source of truth for monitoring status, maturity, and risk exposure.
Operational Excellence & Scaling: Identify execution gaps, tooling limitations, and process inefficiencies.
Drive investments in scalability, reliability, and traceability of monitoring systems.
Support automation and standardization to reduce manual overhead and improve consistency.