Data Science Team Lead, Search & Evaluation  reference: R105207

This review provides an in-depth look at the Data Science Team Lead, Search & Evaluation position at Elsevier, offering potential candidates valuable insights into the opportunity and guiding their career considerations.

About the Role: A Pioneer in AI-Driven Discovery

Elsevier, a global leader in information and analytics, is at the forefront of advancing scientific discovery and improving health outcomes. They are seeking a Data Science Team Lead, Search & Evaluation to join their innovative platform data science organization. This is a pivotal role for an accomplished data scientist who is passionate about shaping the future of knowledge discovery through cutting-edge AI, retrieval systems, and robust evaluation frameworks.

The team is instrumental in driving enterprise-scale AI innovation across Elsevier’s global platforms, including well-known products like Scopus AI and ClinicalKey AI. This role offers a unique chance to lead a team that is not just building search and AI capabilities, but actively pioneering intelligent discovery experiences that impact millions of users worldwide, from researchers accelerating breakthroughs to clinicians making critical, evidence-based decisions.

Key Advantages for Candidates: Opportunities for Impact and Growth

• Leadership and Strategic Influence: This role offers the chance to lead and mentor a talented team of data scientists and applied researchers. You will have the opportunity to define and execute the roadmap for enterprise-wide search and retrieval excellence, directly influencing the development of next-generation discovery tools. Your strategic input will be crucial in aligning AI capabilities with the evolving needs of researchers, clinicians, and pharmaceutical professionals.
• Cutting-Edge Technology and Innovation: You will be working at the intersection of advanced AI technologies, including lexical, vector, and hybrid retrieval systems, as well as Retrieval-Augmented Generation (RAG). This is an excellent opportunity to deepen your expertise in areas like dense embeddings, neural re-ranking, cross-encoder models, and integrating LLMs with vast datasets.
• Impactful Work on a Global Scale: The search and AI solutions you help develop will power discovery for millions of users across critical domains like research, life sciences, and healthcare. This provides a significant opportunity to make a tangible impact on global scientific advancement and patient care.
• Emphasis on Rigorous Evaluation and Responsible AI: A core aspect of this role is owning and defining the evaluation framework for retrieval and generative AI systems. This includes a strong focus on metrics like factual consistency, hallucination rates, and human-in-the-loop quality ratings. You will also play a key role in embedding fairness, bias detection, and ethical considerations into all AI systems, ensuring transparency and trust, aligning with Elsevier’s Responsible AI standards.
• Collaborative and Cross-Functional Environment: You will partner closely with product, engineering, and data platform leaders, fostering a culture of collaboration. Additionally, you'll have the chance to integrate scientific taxonomies, citation networks, and clinical ontologies, working with domain experts to enrich and contextualize the discovery experience.
• Commitment to Employee Well-being and Development: Elsevier demonstrates a strong commitment to work-life balance and employee well-being. They offer flexible working hours, comprehensive benefits including a pension plan, generous vacation, parental leave, study assistance, and a personal choice budget. These initiatives underscore their dedication to supporting employees' immediate responsibilities and long-term career aspirations.

Key Considerations for Candidates: Navigating the Opportunity

• Required Expertise: Candidates are expected to possess a PhD or MSc in a relevant field with significant experience (6+ years) in building and evaluating search, ranking, or retrieval systems, including at least 2 years in a leadership or senior technical capacity. Deep expertise in lexical search, vector retrieval, and RAG system design is essential. Proficiency in Python and relevant libraries like PyTorch, Hugging Face, LangGraph, or Haystack is a must.
• Production Deployment Experience: While not strictly mandatory, preferred candidates will have experience deploying retrieval-enhanced LLMs and hybrid retrieval pipelines in production environments. Familiarity with scientific ontologies and metadata standards will be a significant advantage.
• Strong Communication and Stakeholder Management: Given the cross-functional nature of the role, excellent communication and stakeholder management skills are crucial. The ability to effectively bridge the gap between data science, engineering, and product teams will be key to success.
• Domain Knowledge: Prior experience in academic publishing, research intelligence, or enterprise-scale AI systems is highly valued, as it will provide a strong foundation for understanding Elsevier’s complex data landscape and user needs.
• Commitment to Responsible AI: For candidates passionate about ethical AI development, this role offers a significant opportunity to champion and implement responsible AI practices within a leading information services organization.

In Summary: A Transformative Career Move

The Data Science Team Lead, Search & Evaluation position at Elsevier presents a compelling opportunity for ambitious data science leaders. It offers the chance to drive innovation in AI-powered discovery, lead a talented team, and make a substantial impact on how scientific and health information is accessed and utilized globally. With a strong emphasis on cutting-edge technology, rigorous evaluation, and employee well-being, this role is well-suited for individuals looking to advance their careers in a dynamic and mission-driven environment.