Senior AI Governance Specialist

About Northern Trust

As a global leader in innovative wealth management, asset servicing, asset management and banking services, Northern Trust (Nasdaq: NTRS) is proud to guide the world’s most successful individuals, families, corporations and institutions.

Since 1889, we have aligned our efforts with our three guiding Principles That Endure: Service, Expertise, and Integrity. Together, they reflect the three cornerstones of business conduct which we strive to instill in our employees, whom we call partners, and to provide to our clients and the communities we serve worldwide.

With more than 135 years of financial experience and over 24,000 partners, we serve the world’s most sophisticated clients using leading technology and exceptional service.

Senior AI Governance Specialist, ASSET MANAGEMENT

Northern Trust Asset Management (NTAM) is an over a trillion-dollar investment management firm entrusted by investors around the globe to help them navigate changing market environments, so they can confidently realize their long-term objectives.

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.

KEY DUTIES AND RESPONSIBILITIES

Support projects and establish responsible, safe AI practice across NTAM. Responsibilities include:

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, ie 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.

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.

Bachelor's degree in data science, engineering, risk, law, business or a related field.

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.

KEY BEHAVIORAL COMPETENCIES

Business acumen, and the ability to communicate to executives, business domain stakeholders and technical staff.

Strong project management, acumen and familiarity working in agile environments.

Experience with innovation and ideation methods, such as design thinking and product management.

Demonstrated influence, communication, presentation, and facilitation skills.

Demonstrate the ability to work well with others and exhibit leadership.

Be an excellent communicator and collaborator, engaging with multiple technical and business stakeholders and leaders. Team player with collaborative and inclusive approach.

EXPERIENCE AND EDUCATION

10+ years in data governance, model risk, technology risk, compliance or a related analytical role.

Bachelors degree in field like Computer Science, Information technology, Math, Data Science, Engineering, Statistics, Finance (with focus on analysis) etc. and/or relevant proven work experience is required

Working with Us

As a Northern Trust partner, you will be part of a flexible