Data Discovery and Enrichment Expert II

Are you passionate about research and eager to grow your skills in a dynamic, international environment?

Do you want to contribute to ensuring that innovative Generative AI (GEN AI) solutions meet high standards of trust, quality, and reliability—helping advance science and education globally?

About Our Team

Our team plays a key role in supporting GEN AI development evaluations for Elsevier’s Academic and Government (A&G) division. We maintain rigorous quality standards for delivered content, empowering researchers, institutions, and governments to foster discovery and innovation.

About the Role

You will support LeapSpace, a flagship product providing researchers with intelligent, contextual, and personalized assistance throughout their research lifecycle—from ideation and funding acquisition to planning, analysis, and writing. This project is central to Elsevier’s vision of transforming research workflows with AI.

Responsibilities

• Assist in designing and executing development evaluations for GEN AI products within Elsevier’s Academic and Government (A&G) division.

• Help ensure evaluation processes align with Elsevier’s Evaluation playbook and Responsible AI principles.

• Collaborate with data scientists, product managers, and other stakeholders to support evaluation criteria and data collection.

• Analyze evaluation data and contribute to actionable insights and recommendations.

• Support efforts to ensure AI outputs are accurate, explainable, unbiased, and equitable.

• Communicate evaluation results clearly to both technical and non-technical audiences.

• Contribute to continuous process improvements and knowledge sharing within the team.

• Learn from and collaborate with senior team members and cross-functional teams.

Requirements

• Bachelor’s degree in engineering.

• Some experience or internships in data analysis, AI/ML evaluation, or related areas is a plus.

• Basic understanding of Responsible AI principles such as fairness, transparency, and accountability.

• Familiarity with statistical analysis, experimental design, and evaluation metrics is desirable.

• Strong desire to learn and develop technical and collaboration skills.

• Good communication skills and ability to work effectively in teams.

• Detail-oriented with a problem-solving mindset.

• Interest in academic research lifecycle and publishing is a bonus.

Reporting Line

You will report to the Senior Manager of Data Discovery and Enrichment Experts within the AI Evaluation, DDE and Process Innovation division.

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.

• Working flexible hours - flexing the times when you work in the day to help you fit everything in and work when you are the most productive

Working for you

• Dutch Share Purchase Plan

• Annual Profit Share Bonus

• Comprehensive Pension Plan

• Home, office or commuting allowance

• Generous vacation entitlement and option for sabbatical leave

• Maternity, Paternity, Adoption and Family Care leave

• Flexible working hours

• Personal Choice budget

• Variety of online training courses and career roadshows

• Wellbeing programs and gym facility in the office

• Internal communities and networks

• Various employee discounts

• Recruitment introduction rewards

• Work from anywhere

• Employee Assistance Program (global)

• Annual Event

About Business

At Elsevier, we help the world of research to make high-value decisions with confidence through a rich portfolio of services and products such as Scopus, Pure, and SciVal. The high-quality connected data and insights that these products provide come from our Research Data Platform (RDP), the data powerhouse of Elsevier’s analytics business. RDP follows Data Mesh principles and is organized in domains, one of which is focusing on identifying and linking topics and concepts found in scientific text.