Manager Data Science
- Compensation
- $115.4K–$192.3K / yr
- Location
- Philadelphia, PA
- Work setup
- Not specified
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- Not specified
- Category
- Data science
- Posted
Skills
Must have
- 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.
Full description
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.