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About the Role

We are seeking 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.

Responsibilities

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.
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) and 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.
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.
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.
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.

Requirements

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)
Strong hands-on experience with RAG pipelines and LLM-based systems
Strong hands-on experience with 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

Skills

Search and retrieval systems (lexical, vector, hybrid)
RAG pipelines
LLM-based systems
Evaluation methodologies for ML / IR / GenAI
Python
PyTorch
Hugging Face
LangChain
LangGraph
Haystack
Databricks
Experimentation design
Statistical analysis
Large-scale datasets
Scientific ontologies
Metadata standards
Production ML systems
MLOps practices
Data visualization
Analytical tooling
Human-in-the-loop evaluation
Annotation workflows
Information retrieval
NLP
Generative AI

Location

NLD Amsterdam (Radarweg)

Work Type

Flexible working hours

Experience Level

Senior
Individual contributor

Education Level

Master’s degree
PhD

Salary/Compensations

€53,800 - €89,900

Benefits

Shared parental leave
Study assistance
Sabbaticals
Holiday allowance with the option to buy additional days
Health screening
Eye care vouchers
Private medical benefits
Life assurance
Optional additional life cover
Spouse's life cover at own cost
Competitive contributory pension scheme
Save As You Earn share option scheme
Optional self funded benefits
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
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 charities and causes
Awards to recognize key service milestones

About the Company

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.
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.

Equal Opportunity

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