Manager Data Science
AI in this role
AI for Science, Research Intelligence & Knowledge Discovery
Lead the Teams Building AI That Advances Science
What if the teams you lead could help accelerate scientific breakthroughs, improve healthcare outcomes, and expand human knowledge?
At Elsevier, data science leadership is about far more than managing projects, models, or roadmaps. It is about leading teams that build intelligent systems enabling researchers, clinicians, educators, and institutions to discover evidence, connect ideas, uncover insights, and solve some of the world's most important challenges.
Every day, millions of researchers depend on our products to navigate an ever-growing universe of scientific knowledge. As a Data Science Leader, your work will directly influence how knowledge is discovered, understood, trusted, and applied across the global research ecosystem.
This is leadership with purpose. This is AI in service of science.
About the team
As part of a growing team of Data Scientists, you will take on some of the hardest problems in science. This team is building intelligent systems that can reason across scientific publications, research data, knowledge graphs, ontologies, metadata, taxonomies, citations, and content spanning every scientific discipline.
About the Role
As a Data Science Leader, you will build, develop, and inspire high-performing teams responsible for delivering advanced AI, machine learning, search, retrieval, NLP, and generative AI solutions that power scientific discovery and research intelligence.
You will provide strategic direction, elevate technical excellence, and help shape the future of AI-enabled products used by researchers and healthcare professionals worldwide. Working at the intersection of cutting-edge technology and meaningful impact, you will guide teams solving some of the most complex and intellectually challenging problems in science.
Success in this role requires a balance of technical depth, people leadership, strategic thinking, and a passion for helping others do their best work while advancing a mission that matters.
What You'll Do
- Lead and develop high-performing teams of data scientists, machine learning engineers, researchers, and technical contributors.
- Define and execute data science strategies that advance scientific discovery, research intelligence, and knowledge-access products.
- Drive the development of AI-powered capabilities across search, retrieval, recommendation, NLP, knowledge systems, and generative AI.
- Translate complex customer, scientific, and business challenges into scalable data science solutions and measurable outcomes.
- Establish high standards for experimentation, evaluation, model quality, reliability, and responsible AI practices.
- Partner closely with Product, Engineering, Research, UX, Analytics, and domain experts to shape product strategy and delivery.
- Mentor and coach team members while fostering a culture of scientific rigor, collaboration, innovation, and continuous learning.
- Guide the adoption of emerging AI technologies, including LLMs, retrieval-augmented generation, semantic search, and knowledge-based systems.
- Influence senior stakeholders and contribute to long-term AI, technology, and product strategy across the organization.
- Ensure that AI systems are trustworthy, scalable, explainable, measurable, and aligned with meaningful customer and societal outcomes.
What We're Looking For
- Significant experience leading data science, machine learning, artificial intelligence, NLP, information retrieval, or related technical teams.
- Proven success building, coaching, and developing high-performing teams in complex technology or product environments.
- Technical expertise across machine learning, generative AI, large language models, retrieval systems, experimentation, and model evaluation.
- Experience delivering AI-powered products or platforms from concept through production deployment and measurable impact.
- Deep understanding of modern AI approaches, including LLMs, RAG architectures, semantic search, embeddings, and knowledge systems.
- Experience establishing evaluation frameworks, experimentation practices, and performance metrics for AI solutions.
- Ability to translate ambiguous challenges into clear strategy, execution plans, and business outcomes.
- Exceptional communication and stakeholder-management skills with the ability to influence technical, product, and executive audiences.
- Experience working with large-scale structured, semi-structured, and unstructured data in production environments.
- A passion for advancing science, expanding access to knowledge, developing people, and applying AI to create meaningful real-world impact.
Why Join Elsevier
Because your leadership will matter.
You will lead teams building AI systems that help researchers discover knowledge faster, assess evidence more effectively, generate new insights, and accelerate scientific progress.


U.S. National Base Pay Range: $115,400 - $192,300. Geographic differentials may apply in some locations to better reflect local market rates.

If performed in Maryland, the base pay range is $121,200 - $201,900.If performed in New York, the base pay range is $126,900 - $211,500.If performed in New York City, the base pay range is $138,400 - $230,700.If performed in Rochester, NY, the base pay range is $115,400 - $192,300.If performed in New Jersey, the base pay range is $136,213 - $217,587.

This job is eligible for an annual incentive bonus.





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How we rate this
Manager Data Science at RELX rates 93 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.
Prepare for this job
A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.
Skills and AI tools this role asks for
Questions you could be asked
- How would you design a retrieval step so the model answers from real data instead of guessing?
- How do you decide that one model's output is better than another's for a given task?
- What NLP problem have you worked on, and how did you measure whether it actually worked?
- How do you think about the risk of an AI system in this kind of role failing silently?
- How would you decide a model or AI system is ready to ship?
Adapt your resume
- List these exact terms on your resume: RAG, AI Evaluation, NLP, and AI Safety. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.
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