About the Role
As an AI Governance Manager, you will play a key role in operating and enhancing AI governance processes within Data Science. You will partner with data scientists, model owners, and governance stakeholders to support model approvals, inventory management, regulatory compliance, and the continued evolution of a scalable governance framework that enables responsible AI innovation.
Responsibilities
- Own and run day-to-day AI/model approval and review processes, ensuring consistent and timely turnaround.
- Maintain the third-party software and model inventory model inventory as the authoritative record of models in development and production.
- Act as the primary day-to-day point of contact for AI software requesters, data scientists and model owners navigating governance requirements.
- Track and report on the health of the model portfolio, 3rd party AI software and the effectiveness of approval processes.
- Manage and continuously improve governance products supporting approvals and inventory management.
- Identify opportunities for automation, self-service capabilities, and process improvement.
- Support the evolution of governance policies, standards, and risk assessment methodologies.
- Maintain audit-ready documentation and support compliance, audit, and risk management activities.
Requirements
- A data science background with hands-on experience building, training, validating, or deploying machine learning models, gained as a data scientist, ML engineer, or in a closely related technical role.
- Experience with, or strong interest in, AI/model governance, model risk management, or a related risk/compliance discipline.
- Strong organizational skills and comfort owning operational processes end-to-end, including maintaining structured records such as a model inventory.
- Practical understanding of relevant AI/data regulation and standards.
- A continuous-improvement mindset.
- Strong communication skills, able to explain governance requirements clearly to technical model owners and translate technical detail for non-technical stakeholders.
- Ability to work independently on operational delivery while contributing constructively to longer-term framework design.
- Prior experience with model inventory, MLOps, or model risk management tooling, and exposure to governance framework or policy development.
Work in a Way That Works for You
We promote a healthy work/life balance across the organisation. We offer an appealing working prospect for our people. With numerous wellbeing initiatives, shared parental leave, study assistance, and sabbaticals, we will help you meet your immediate responsibilities and your long-term goals.
About the Business
A global leader in information and analytics, we help researchers and healthcare professionals advance science and improve health outcomes for the benefit of society. Building on our publishing heritage, we combine quality information and vast data sets with analytics to support visionary science and research, health education and interactive learning, as well as exceptional healthcare and clinical practice. At Elsevier, your work contributes to the world's grand challenges and a more sustainable future. We harness innovative technologies to support science and healthcare to partner for a better world.
We know your well-being and happiness are key to a long and successful career. We are delighted to offer country specific benefits. Click here to access benefits specific to your location.
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Skills Required
- Background in data science with hands-on experience building, training, validating, or deploying machine learning models
- Experience with or strong interest in AI/model governance, model risk management, or related risk and compliance disciplines
- Strong organizational skills and experience owning operational processes end-to-end
- Experience maintaining structured records such as a model inventory
- Practical understanding of relevant AI and data regulations and standards
- Strong communication skills for explaining governance requirements to technical and non-technical stakeholders
- Ability to work independently on operational delivery and contribute to framework design
- Prior experience with model inventory, MLOps, or model risk management tooling
- Exposure to governance framework or policy development
- Continuous-improvement mindset
Elsevier Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Elsevier and has not been reviewed or approved by Elsevier.
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Leave & Time Off Breadth — Feedback suggests paid time off spans vacation, holidays, sick days, bereavement, military leave, and volunteer time. Family-related leave options are also emphasized as part of the package.
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Healthcare Strength — Feedback suggests medical, dental, vision, life insurance, wellness initiatives, and an EAP form a comprehensive health offering. Gym support and wellbeing hubs reinforce an ongoing health and wellness focus.
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Retirement Support — Feedback suggests retirement programs include a 401(k)/retirement plan and pension options, with long-term savings vehicles such as an employee stock purchase plan also available. These elements contribute to a sense of financial security beyond base pay.
Elsevier Insights
What We Do
Elsevier is a world-leading provider of information solutions that enhance the performance of science, health, and technology professionals, empowering them to make better decisions, and deliver better care. Because informed decisions lead to better outcomes, Elsevier is a leader in information and analytics for customers across the global research and health ecosystems. Elsevier helps researchers and healthcare professionals advance science and improve health outcomes for the benefit of society. We do this by facilitating insights and critical decision-making for customers across the global research and health ecosystems.
