Frost MetaBrain: Governance & Trust Engineering

Bring your research and consulting expertise into MetaBrain solution development. Turn industry knowledge and advisory methods into reusable, continuously updated software-enabled services.

MetaBrain combines Frost & Sullivan’s 60 years of industry expertise, enterprise data, and third-party insights, delivering human-reinforced AI insights and analytics to solve your toughest business use cases with actionable, real-time intelligence.

https://metafrost.ai/

Key responsibilities

Reporting to Head of Decision Intelligence Governance & Trust Engineering, with access to privacy, security and advisory approvers, the incumbent will make research-to-software solutions trustworthy by turning research standards, data permissions and AI risks into practical controls, test evidence and accountable release decisions. Protect client confidence without replacing professional judgment with a checklist.

• Govern evidence and permitted use. Maintain registers for datasets, models and decision workflows. Track ownership, source licences, consent or other applicable permissions, confidentiality, retention, approved uses and restrictions, with legal/privacy review where needed.
• Define the assurance standard. Create research-quality, evidence, uncertainty and human-review requirements. Design tests for unsupported claims, inaccurate citations, numerical errors, bias and unsuitable recommendations; record exceptions and remediation.
• Translate policy into controls. Specify role permissions, approval gates, audit events and tenant-isolation tests with engineers. The engineering variant implements and tests controls, logging, data masking and policy checks in approved environments.
• Support accountable operations. Maintain risk assessments, model documentation, change records and release evidence. Coordinate red-team findings, incident escalation, rollback exercises and advisor acceptance; report risks independently from delivery pressure.

Essential requirements

• A strong record in research quality, consulting delivery assurance, audit, risk, data governance or a related field; typically 4+ years, with equivalent achievement considered. Clear evidence-based writing, structured risk judgment and constructive challenge. Basic AI learning plus a practical ability to identify and explain AI output failures.
• Preferred: Exposure to responsible AI, privacy-by-design, security testing, data provenance or AI assurance frameworks. SQL/Python, access-control testing and logging experience are useful; hands-on coding and control implementation are required for appointment to the Engineer variant.
• Entry level and authority: Governance specialists own requirements and assurance evidence with technical partners; they are not automatically security engineers, legal counsel or the data protection officer. Risk acceptance and production release remain with formally delegated senior owners.