At a Glance
- Tasks: Lead the development of AI governance frameworks to ensure responsible innovation.
- Company: Join Elsevier, a global leader in information and analytics with an inclusive culture.
- Benefits: Enjoy flexible work options, wellbeing initiatives, and career development support.
- Other info: Be part of a mission-driven team contributing to science and healthcare advancements.
- Why this job: Make a real impact on AI governance while balancing innovation and compliance.
- Qualifications: Experience in AI governance, strong stakeholder management, and knowledge of regulations.
The predicted salary is between 75600 - 92400 £ per year.
Are you passionate about building and owning practical, scalable AI governance frameworks that enable innovation while managing risk?
About the Role
As Model Governance Director, Data Science, you will own and evolve the end-to-end AI and model governance framework on behalf of the Data Science function. You will provide strategic leadership, helping to ensure AI systems are developed and deployed responsibly while balancing regulatory compliance, risk management, innovation, and business outcomes. This role partners closely with technical, legal and product stakeholders to embed effective governance into how we use AI.
Responsibilities
- Own the end-to-end AI and model governance framework on behalf of the Data Science function, including policy implementation and continuous improvement.
- Define standards for model risk classification, approvals, documentation, monitoring, and lifecycle management.
- Set the strategic roadmap for AI governance tools and processes.
- Ensure compliance with relevant AI regulations and standards, including the EU AI Act, GDPR, and emerging global requirements.
- Partner with Legal, Compliance, Privacy, and Information Security teams to maintain governance policies.
- Act as a key contact for audits, regulators, customers, and industry forums on AI governance matters.
- Establish governance metrics, reporting, model inventory oversight, and operational effectiveness measures.
- Lead and develop governance capability while supporting training and awareness across the organisation.
Requirements
- Significant experience in AI/model governance, model risk management, data governance, compliance, or a related risk discipline, including people management.
- Strong knowledge of AI governance regulations and frameworks (EU AI Act, ISO 42001 or similar) and their practical application.
- Experience designing and operating governance processes for AI/ML models in production environments.
- Strong commercial judgment with the ability to balance innovation, risk, and business objectives.
- First class stakeholder management and communication skills, with experience influencing senior technical and business leaders.
- Proven track record of creating scalable governance processes, driving continuous improvement, and introducing automation.
- Working knowledge of the AI/ML lifecycle, including data sourcing, model development, validation, deployment, and monitoring.
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.
Model Governance Director, Data Science employer: Elsevier
At Elsevier, we pride ourselves on being an excellent employer, particularly for our Java Software Engineer role in Oxford. Our vibrant work culture fosters collaboration and innovation, while our commitment to employee growth is evident through continuous learning opportunities and flexible working arrangements that promote a healthy work-life balance. Join us to be part of a team that values your contributions and supports your professional journey in the exciting field of scientific knowledge sharing.
StudySmarter Expert Advice🤫
We think this is how you could land Model Governance Director, Data Science
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We think you need these skills to ace Model Governance Director, Data Science
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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Craft a Tailored Cover Letter:For a full-time role at Elsevier, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Elsevier. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Elsevier
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
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Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
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✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
