Sr AI Governance Specialist

Job Summary

Senior AI Governance Specialist, ASSET MANAGEMENT

NTAM has embarked on an ambitious plan to modernize its Data and AI capabilities and embed a data-driven culture throughout the organization. The Data Governance Specialist will pride themselves in taking a customer first approach to truly make it easier for our internal customers to protect, enhance and instill trust in our data to generate insight and deliver the best client experience. The key focus of this role is to assist the company adopt AI quickly and safely. You will turn AI governance policy and standards into practical controls, assess AI use cases and models for risk, and give business teams clear guidance on what good looks like. You will work across Data, Technology, Risk, Legal and the business, reporting to the Head of Data and Analytics.

Responsibilities

Data management

• Check that data used in AI systems has a clear owner, documented lineage, quality checks and appropriate access controls.
• Explicit evaluation of data completeness, suitability, ownership, lineage, quality and governance.
• Maintain the AI inventory and link each model to its source data, classification and retention requirements. Extend inventory coverage beyond models and use cases to AI systems, platforms, agents, agentic solutions, digital workers and vended AI capabilities.

AI model risk management

• Work closely with the Enterprise Model Risk Management teams and Front line AI model governance team to run risk assessments on AI use cases and models, covering bias, explainability, privacy, security and third-party risk.
• Support model validation, ongoing monitoring and issue tracking in line with model risk principles (independent challenge, documentation, performance and drift thresholds).
• Assist to develop frameworks to map controls to applicable regulations and standards, such as the EU AI Act and NIST AI RMF and linked up with our NT AI Policy and Standards.
• Importantly, the role is about training NTAM AI key stakeholders, i.e., model owner, engineers, architects and data practitioners on how to perform these actions and ensure controls are in place so that this becomes a repeatable process.

Safe AI adoption

• Review new AI requests, advise teams on risk tiers and required controls, and escalate high-risk cases.
• Develop guidance, templates and training that make responsible AI use easy for non-technical colleagues.
• Report governance metrics and emerging risks to senior stakeholders and the AI governance committee.

AI telemetry and guardrail monitoring

• Help define and maintain the telemetry that shows AI systems are behaving as intended, such as usage, prompt and output flags, policy violations, model drift, error rates and human override rates.
• Review dashboards and alerts to confirm guardrails (content filters, access limits, data loss controls) are working as expected, and investigate gaps or false positives with engineering teams.
• Log and track incidents through to resolution, and feed lessons back into controls, thresholds and risk assessments.
• Report back to Senior Management on AI portfolio telemetry to ensure that there is transparency from different AI initiatives (prioritized by risk tiering) with clear action owner, target date, closure evidence and escalation points, rather than only tracking incidents through resolution.

Training and change management

• Design and deliver AI governance and responsible AI training for employees, managers and technical teams, tailored to each audience.
• Lead change management for governance rollouts: stakeholder mapping, communications, champions network and feedback loops.
• Measure adoption and behavior change (training completion, policy adherence, intake quality) and adjust the approach to improve it.
• Mentor junior analysts and set practice standards for the team.
• Interface with AI Intake process to check submissions for completeness, perform initial classification and routing using defined criteria, challenge unsupported assertions or unclear ownership, and escalate incomplete or potentially higher-risk submissions.

Cross-functional coordination

• Explicitly include Technology, Data, Privacy, Cyber, Model Risk, Operational Risk, Compliance, Legal, TPRM and business teams, with responsibility for following up on information, actions and evidence.

Continuous improvement

• Identify improvements to templates, trackers, reporting processes and documentation quality to make oversight more consistent, efficient and scalable.

What Success Looks Like

• Within 6 months: the AI inventory is complete and every use case is risk-tiered.
• Business teams get governance guidance quickly and rate the process as clear and useful.
• High-risk models have documented validation and active monitoring.

Knowledge and Skills

• Working knowledge of data management practice: data quality, lineage, metadata, classification and privacy.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.