Elsevier
Senior Data Scientist I - LeapSpace
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About the team
Elsevier’s mission is to help researchers, clinicians, and life sciences professionals advance discovery and improve health outcomes through trusted content, data, and analytics. As the landscape of science and healthcare evolves, we are pioneering intelligent discovery experiences — from Scopus AI and LeapSpace to ClinicalKey AI, PharmaPendium, and next-generation life sciences platforms. These products leverage retrieval-augmented generation (RAG), semantic search, and generative AI to make knowledge more discoverable, connected, and actionable across disciplines. The Search & AI Evaluation team sits within the Platform Data Science organization and is responsible for advancing enterprise-scale search, retrieval, and evaluation capabilities across Elsevier's global products.
About the role
We are looking for a Senior Data Scientist I to lead the development and evaluation of advanced search and generative AI systems. You will own complex problem areas end-to-end, drive methodological rigor in evaluation, and contribute to the technical direction of retrieval and RAG systems.
This role is ideal for someone with deep hands-on experience in search/retrieval systems, RAG pipelines, and evaluation frameworks, who is ready to operate as a senior individual contributor with growing technical leadership responsibilities.
Key responsibilities
Search & Retrieval Development
- Play a leading role in the design and optimization of lexical, vector, and hybrid retrieval systems at scale.
- Help architect and improve RAG pipelines, including retrieval strategies, prompt design, and system orchestration (e.g., LangGraph-based workflows).
- Help drive experimentation with embeddings, re-ranking models, and retrieval architectures to significantly improve relevance and user outcomes.
- Partner with engineering to ensure robust, scalable, and production-ready implementations.
Evaluation & Experimentation
- Help define and evolve evaluation strategies for search and generative AI systems across products.
- Help design robust frameworks for:
- IR evaluation (e.g., NDCG, recall, ranking quality)
- GenAI evaluation (e.g., grounding, faithfulness, hallucination detection)
- Contribute to development of evaluation datasets, gold standards, and annotation strategies.
- Guide and review experimental design, including offline evaluation and A/B testing, ensuring statistical rigor and validity.
- Contribute to responsible AI practices, including bias, fairness, and risk evaluation.
Generative AI & Applied Research
- Apply and adapt state-of-the-art techniques in NLP, embeddings, and generative AI to production use cases.
- Evaluate and integrate emerging technologies into the team’s roadmap.
- Contribute to knowledge graph and semantic enrichment efforts that support retrieval systems.
Domain & Research Integration
- Collaborate with domain experts, ontology engineers, and biomedical informaticians to integrate scientific taxonomies, citation networks, and clinical ontologies into retrieval systems.
- Incorporate structured data — including datasets, chemical entities, genes, drugs, clinical trials, and patient outcomes — into AI-powered discovery pipelines.
- Advance Elsevier’s knowledge graph and metadata integration strategy, linking research and health data for more context-aware retrieval.
- Apply cutting-edge research in information retrieval, NLP, embeddings, and generative AI to continuously evolve Elsevier’s discovery and evaluation stack.
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Collaboration & Delivery
- Work closely with product, engineering, and domain experts to define and deliver impactful solutions.
- Communicate findings and recommendations clearly to both technical and non-technical stakeholders.
- Take ownership of projects from problem definition through experimentation and deployment.
Required qualifications
- Master’s or PhD in Computer Science, Data Science, Machine Learning, or a related field (or equivalent practical experience)
- Experience in data science, machine learning, or applied NLP
- Strong hands-on experience with:
- Search and retrieval systems (lexical, vector, hybrid)
- RAG pipelines and LLM-based systems
- Evaluation methodologies for ML / IR / GenAI
- Advanced programming skills in Python
- Experience with modern ML/NLP frameworks (e.g., PyTorch, Hugging Face, LangChain, LangGraph, Haystack)
- Experience working with Databricks or similar distributed data/ML platforms
- Strong understanding of experimentation design and statistical analysis
Preferred qualifications
- PhD in Computer Science, Data Science, Machine Learning, or a related field
- Experience working with large-scale datasets (scientific, biomedical, or enterprise data)
- Familiarity with scientific ontologies and metadata standards (e.g., MeSH, UMLS, ORCID, CrossRef)
- Exposure to production ML systems and MLOps practices
- Familiarity with data visualization and analytical tooling (e.g., Tableau, Power BI, matplotlib, seaborn, or similar) to communicate insights effectively
- Experience with human-in-the-loop evaluation or annotation workflows
- Publications or demonstrated applied research in IR, NLP, or generative AI
Why join us?
Join our team and contribute to a culture of innovation, collaboration, and excellence. If you are ready to advance your career and make a significant impact, we encourage you to apply.
Work in a way that works for you
We promote a healthy work/life balance across the organization. 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.
- Flexible working hours - flexing the times when you work in the day to help you fit everything in and work when you are the most productive.
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Working for you
We know that your well-being and happiness are key to a long and successful career. These are some of the benefits we are delighted to offer:
- Holiday allowance with the option to buy additional days
- Health screening, eye care vouchers and private medical benefits
- Life assurance, plus optional additional life cover and spouse's life cover at own cost
- Access to a competitive contributory pension scheme
- Save As You Earn share option scheme
- Access to optional self funded benefits, including electric vehicle scheme, cycle to work scheme, dental insurance, critical illness cover, health cash plan, personal travel insurance
- Travel season ticket loan
- Paid time off when you become a parent, and paid time off for carers
- Support for personal and work-related challenges
- Access to emergency care for both the elderly and children
- Time off to support the charities and causes that matter to you
- Awards to recognize key service milestones
About the business
As 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.
If performed in NLD Amsterdam (Radarweg), the base pay range is €53,800 - €89,900. This job may be subject to a collective labor agreement in the Netherlands. Please consult with the hiring team for further details.
We know your well-being and happiness are key to a long and successful career. We are delighted to offer country specific benefits. Click here to access benefits specific to your location.
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We are an equal opportunity employer: qualified applicants are considered for and treated during employment without regard to race, color, creed, religion, sex, national origin, citizenship status, disability status, protected veteran status, age, marital status, sexual orientation, gender identity, genetic information, or any other characteristic protected by law.
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