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 65 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.
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.