AI Governance

Overview

In this role within PA’s Digital & Data community, you will help clients harness AI responsibly at scale by shaping governance, risk management, and regulatory alignment. You’ll work with AI specialists, data scientists, and cross-functional teams to design, implement, and operate AI governance frameworks. The position offers hybrid working, talent development, and exposure to diverse sectors, with a focus on delivering measurable value and safe innovation. You will contribute to client outcomes while growing your expertise in governance and responsible AI.

Pay / Benefits

• Private healthcare for you and family
• 25 days annual leave (+ 5 days option)
• Generous pension scheme
• Annual performance-based bonus
• PA share ownership
• Tax-efficient benefits (cycle to work, give as you earn)

Responsibilities

• Design, implement and operate AI governance frameworks for clients
• Conduct AI risk assessments, model governance reviews, and responsible AI evaluations
• Navigate AI regulations and standards (EU AI Act, NIST RMF, ISO/IEC 42001/23894, OECD AI)
• Facilitate governance forums, risk assessments, and operating model design with senior stakeholders
• Collaborate across cross-functional teams and client sites (hybrid work)
• Support internal AI governance programs and governance tooling initiatives
• Provide input on data usage, security, privacy, bias, and model risk in Generative AI contexts

Key requirements

• Experience delivering AI governance, responsible AI, AI risk management, or model governance in private sector
• Consulting background with Life Sciences, Government or Consumer & Manufacturing sectors preferred
• Proven ability to communicate with business, legal, compliance, risk, IT, security, data science, and engineering teams
• Experience with AI lifecycle governance (development, validation, deployment, monitoring, explainability)
• Familiarity with Generative AI governance considerations (data usage, IP, security, privacy)
• Analytical mindset and ability to produce quality, results‑driven work

• problem-solving
• stakeholder collaboration
• analytical thinking
• AI governance frameworks
• Regulatory interpretation (EU AI Act, NIST AI RMF, ISO/IEC 42001/23894, OECD AI Principles)
• AI risk assessments and model governance

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