Senior AI Governance Specialist

Overview

Senior AI Governance Specialist in Northern Trust Asset Management (NTAM) to help adopt AI quickly and safely by turning AI governance policy and standards into practical controls, assessing AI use cases and models for risk, and giving business teams clear guidance. Reports to the Head of Data and Analytics.

What you'll do

• Check that data used in AI systems has a clear owner, documented lineage, quality checks and appropriate access controls.
• Explicitly evaluate 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.
• 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, linked to the NT AI Policy and Standards.
• Train NTAM AI key stakeholders (model owners, engineers, architects and data practitioners) on how to perform these actions and ensure controls are in place so this becomes a repeatable process.
• 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.
• Help define and maintain AI telemetry 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, prioritized by risk tiering, with clear action owner, target date, closure evidence and escalation points.
• 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 the 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.
• Coordinate across Technology, Data, Privacy, Cyber, Model Risk, Operational Risk, Compliance, Legal, TPRM and business teams, following up on information, actions and evidence.
• Identify improvements to templates, trackers, reporting processes and documentation quality to make oversight more consistent, efficient and scalable.

What you'll need

• 10+ years in data governance, model risk, technology risk, compliance or a related analytical role.
• Bachelor's degree in Computer Science, Information Technology, Math, Data Science, Engineering, Statistics, Finance (with focus on analysis) or a related field, and/or relevant proven work experience.
• Working knowledge of data management practice: data quality, lineage, metadata, classification and privacy.
• Proven domain knowledge in financial services.
• Understanding of model risk principles and the AI/ML lifecycle, including validation, monitoring and bias testing.
• Ability to read a policy or regulation and turn it into clear, testable controls.
• Strong written and verbal communication; comfortable advising senior and non-technical audiences, with a track record of delivering training and driving adoption of new ways of working.
• Practical, pragmatic mindset that enables adoption rather than blocking it.

Nice to have

• Experience in a regulated industry such as financial services, healthcare or insurance.
• Familiarity with frameworks such as NIST AI RMF, ISO/IEC 42001, SR 11-7 or DAMA-DMBOK.
• Certifications such as AIGP, CDMP, CRISC or CIPP.

Details

• Location: Bangalore, India.