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.