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Job Overview

The AI & Cyber Governance Leader is responsible for establishing and operationalizing governance for data, analytics, and AI systems across the enterprise. This role drives policy, controls, and lifecycle oversight for traditional analytics and machine learning (ML), including generative AI use cases. The manager will align stakeholders in IT, Security, Legal, Compliance, Privacy, Audit, HR, and business units to ensure that data and AI solutions are effective, compliant, secure, ethical, and resilient-and that value delivery is measurable and sustainable.

You will lead governance frameworks, set standards, and implement controls for model risk management, data quality and lineage, responsible AI, privacy-by-design, thirdparty risk, and AI operational monitoring

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A Day In The Life

Every day at Hubbell is different and you'll contribute in many ways. On any given day, you'll make a difference by:

• Design, publish, and maintain the AI & Data Governance Framework, integrating NIST AI RMF, ISO/IEC 42001 (AI management systems), ISO/IEC 27001, SOC 2, GDPR/CCPA, and internal risk policies.
• Participate in the AI Governance Council
• Define acceptable use and procurement standards for AI tools (incl. generative AI), covering data protection, IP, confidentiality, model transparency, human oversight, and safe deployment practices.
• Author policies and standards: data classification, data retention, model documentation, bias testing, explainability, incident response, and vendor due diligence for AI/ML.
• Partner with Data Architecture to embed governance into data platforms (e.g., lakehouse, warehouse, analytics, feature stores).
• Build and oversee Model Risk Management (MRM): inventory, tiering, risk assessments, validation/testing, drift/bias monitoring, retraining triggers, and sunsetting procedures.
• Create control libraries for AI/ML (design-to-deploy), including validation, performance metrics, robustness testing, and contingency plans.
• Coordinate privacy impact assessments (PIA/DPIA) and security threat modeling for AI use cases; enforce least privilege and data minimization.
• Partner with Engineering/MLOps to integrate governance into CI/CD for ML models: documentation, approvals, monitoring dashboards, alerts, and rollback.
• Establish model documentation standards: purpose, training data, evaluation metrics, risk assessments, explainability artifacts, and human-in-the-loop procedures.
• Develop training and awareness programs for responsible AI use; guide business teams on compliant AI solution design.
• Manage thirdparty AI vendors, contracts, and ongoing risk monitoring (performance, security, privacy, compliance).
• Define KPIs/KRIs for AI & data governance (e.g., % of AI systems inventoried and risk-tiered, data quality scores, model drift incidents, bias test coverage, PIA completion rate).
• Produce board/executive reporting on AI risk posture, control effectiveness, and program maturity; support internal/external audit requests.
• Drive continuous improvement against a maturity model and benchmark program performance.

What will help you thrive in this role?

• Bachelor's degree in Information Systems, Computer Science, Data/Analytics, or related field; or equivalent experience.
• 7-10+ years in IT risk, data governance, analytics/ML governance, or compliance with at least 3 years leading programs or teams.
• Experience with data governance (catalogs, lineage, quality, stewardship) and AI/ML lifecycle governance (model documentation, validation, monitoring).
• Strong knowledge of risk frameworks (NIST AI RMF, ISO/IEC 42001, ISO 27001, SOC 2) and privacy (GDPR, CCPA).
• Proven ability to orchestrate crossfunctional governance across IT, Security, Legal, Compliance, Privacy, Audit, and business stakeholders.
• Excellent communication skills-able to translate technical risks into business terms and influence senior leadership.

• Governance design: policy writing, control frameworks, RACI, operating models.
• Risk management: assessments, control testing, issue remediation, KRIs.
• Data management: metadata, lineage, quality management, access control.
• AI/ML: model lifecycle, validation/testing, bias/fairness, explainability.
• Privacy & security: PIAs/DPIAs, encryption, anonymization, secure architecture.
• Stakeholder leadership: facilitation, change management, executive reporting.
• Vendor management: due diligence, contract clauses, ongoing monitoring.

Competencies