Data Governance & AI Consultant

This role is a perm role.

Clearance: SC

Location:UK (Hybrid) with travel to client sites 2 days p/w across Bristol, Portsmouth and Taunton

Key

• Defence experience desirable
• Sonar work and underwater mapping
• Hydrographic office experience would be interesting
• Oil & gas also another transferable industry due to work in seas

What you’ll bring (Essential)

• Proven experience in data governance delivery, including:
• Policy & standards, stewardship models, RACI, decision forums.
• Data quality (dimensions, rules, scorecards, monitoring, remediation).
• Metadata & catalogue adoption (business glossary, lineage, classification & sensitivity).
• Privacy & protection in data handling (e.g., DPIAs, retention, access control, auditability).
• Practical understanding of AI assurance, covering:
• Training data preparation and validation (provenance, quality, representativeness, bias mitigation).
• Model selection considerations (interpretability, performance, risk, security constraints).
• Testing & evaluation (appropriate metrics, benchmarking, robustness/fairness/privacy tests, safe red team exercises).
• In life governance (monitoring, drift detection, risk logging, periodic re evaluation and re training with realistic data, change approvals).
• Strong consulting fundamentals: structured problem solving, facilitation, documentation, and stakeholder communication.
• Ability to work under general direction, plan own work, and enable others—aligned to SFIA Level 4 expectations.
• Excellent written/spoken English.

Desirable

• Experience in UK Central Government, Defence, or other regulated/public sector environments.
• Familiarity with established frameworks/standards (e.g., DAMA DMBOK for data; NIST AI RMF 1.0, ISO/IEC 23894 AI risk management, ISO/IEC 27001, UK GDPR/DPA 2018).
• Hands on exposure to one or more platforms: Microsoft Purview, Azure ML, Databricks/MLflow, Azure Fabric, Power BI governance; plus experience with data catalogues (e.g., Collibra, Alation) is a bonus.
• Exposure to MLOps processes (model registry, approval workflows, monitoring, incidents & rollbacks) and risk/control libraries (e.g., bias, robustness, security, IP, privacy).
• Awareness of secure development and deployment constraints (e.g., air gapped networks, protective monitoring, model and data escrow).

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