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Data Scientist AI for Science, Research Intelligence & Knowledge Discovery Technology
Are you excited by the opportunity to use machine learning, NLP, and generative AI to help researchers discover knowledge faster and make better decisions?
Would you enjoy turning complex scientific and business challenges into practical, production-ready AI solutions that create real user value?
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.
This role sits within Elsevier’s Platform Data Science organization, a centralized AI and data science group responsible for advancing intelligent discovery, retrieval, and generative AI capabilities across Elsevier products and platforms. The organization develops foundational AI technologies that power experiences such as:
- LeapSpace – Elsevier’s AI-powered research assistant
- Elsevier’s broader Search & AI Platform
The Platform Data Science organization works at the intersection of:
- Search and retrieval systems
- Generative AI and LLM applications
- AI evaluation and experimentation
- Semantic enrichment and knowledge systems
- Scalable AI platforms and intelligent workflows
About the Role
We are looking for a Data Scientist III to help design, build, and evaluate advanced AI capabilities supporting LeapSpace and Elsevier’s Search & AI Platform initiatives. This role focuses on applied AI development, retrieval systems, and AI evaluation, bringing cutting-edge AI technologies into production experiences used by researchers worldwide.
The role is ideal for someone with hands-on experience in:
- Applied AI
- NLP
- Information retrieval
- LLM-based applications
You will contribute to the next generation of AI-powered scientific discovery tools while working closely with senior data scientists, engineers, product managers, and domain experts across:
- Retrieval systems
- Generative AI
- Reasoning workflows
- Evaluation frameworks
- Experimentations
Key Responsibilities
Applied AI & Research
🔹 Develop and improve LLM-powered research workflows, including:
- Scientific question answering
- Literature summarization
- Semantic exploration & discovery
- Research insight generation
- Citation-aware retrieval & reasoning workflows
🔹 Build and iterate on agentic and multi-step AI workflows using frameworks such as LangGraph and related orchestration tools.
🔹 Apply modern techniques in:
- NLP
- Generative AI
- Embeddings & semantic representations
- Retrieval-augmented generation (RAG)
- AI reasoning & workflow orchestration
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🔹 Evaluate emerging AI models, tools, and frameworks, contributing recommendations for experimentation and adoption.
🔹 Support prompt engineering, grounding strategies, context management, and **hallucination mitigation efforts.
🔹 Integrate scientific metadata, ontologies, and knowledge assets into AI-powered workflows.
Search, Retrieval & RAG Systems
🔹 Design, develop, and optimize search and retrieval pipelines (lexical, vector, hybrid retrieval approaches).
🔹 Contribute to RAG system development by integrating LLMs with trusted scientific and biomedical content.
🔹 Experiment with:
- Embeddings
- Re-ranking models
- Chunking strategies
- Retrieval orchestration
🔹 Improve relevance and answer quality through semantic search, ranking, and knowledge discovery capabilities.
🔹 Collaborate with engineering teams to deploy and scale AI-powered solutions.
AI Evaluation & Experimentation
🔹 Develop and apply evaluation frameworks for:
- IR metrics (e.g., NDCG, recall, precision)
- LLM & RAG evaluation metrics (e.g., grounding, faithfulness, hallucination detection)
🔹 Build and maintain:
- Evaluation datasets
- Benchmark suites
- Annotation workflows
🔹 Conduct offline experiments and contribute to online experimentation & A/B testing.
🔹 Analyze experimental results and communicate findings to stakeholders.
🔹 Promote responsible AI practices focused on quality, reliability, and trust.
Cross-functional Collaboration
🔹 Partner with product managers, engineers, UX researchers, and domain experts to deliver AI capabilities.
🔹 Communicate technical findings clearly to technical and non-technical audiences.
🔹 Contribute to knowledge-sharing and best-practice adoption across the group.
🔹 Support projects from research & experimentation to production deployment.
Required Qualifications
-
Master’s or Ph.D. in:
- Computer Science
- Data Science
- Machine Learning
- NLP
- Information Retrieval
- Related fields
-
2–4 years of experience in:
- Data science
- Machine learning
- Applied NLP
- Information retrieval
- Generative AI
- Related fields
-
Hands-on experience with:
- LLM-based applications & generative AI systems
- RAG pipelines and retrieval systems
- Search & retrieval architectures (lexical, vector, hybrid)
- Evaluation methodologies for IR and generative AI
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Strong programming skills in Python
-
Experience with modern AI/ML frameworks and tools, including:
- PyTorch
- Hugging Face
- LangChain
- LangGraph
- Haystack
-
Experience with Databricks or similar distributed data/ML platforms
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Understanding of:
- Experimentation methodologies
- Evaluation frameworks
- Statistical analysis
-
Proficiency in data visualization and analytical tooling (e.g., Tableau, Power BI, matplotlib, seaborn).
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Ability to independently execute technical projects and contribute to cross-functional initiatives.
Preferred Qualifications
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Experience building:
- AI assistants
- Agentic workflows
- Conversational AI applications
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Experience working on search, ranking, recommendation, or retrieval systems
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Familiarity with:
- Scientific, biomedical, or scholarly datasets
- Knowledge graphs
- Ontologies
- Semantic enrichment systems
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Experience with production ML systems & MLOps practices
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Academic or industry research experience in:
- NLP
- Information retrieval
- Search
- Generative AI
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Experience in content-rich, knowledge-intensive, or highly regulated domains
Working for Elsevier
We prioritize your well-being and happiness, valuing a long and successful career. Some of our benefits include:
- Comprehensive pension plan
- Home, office, or commuting allowance
- Generous vacation entitlement & sabbatical leave
- Maternity, paternity, adoption, and family care leave
- Flexible working hours
- Personal Choice budget
- Internal employee communities & networks
- Various employee discounts
- Recruitment introduction reward
- Employee Assistance Program (global)
Benefits may vary by location. Access country-specific benefits here.
About Elsevier
As a global leader in information and analytics, we enable researchers, healthcare professionals, and innovators to advance discovery and improve health outcomes. Our mission shapes human progress while supporting sustainability.
Company Values:
- Innovative technologies to support science and healthcare.
- Upholding content integrity, reliability, and reproducibility.
- Driving impact through data, analytics, and knowledge.
With 9,500+ employees, 2,300+ technologists, and 60+ global locations, Elsevier stands united in serving scientific and healthcare communities worldwide.
Join us at Elsevier to inspire progress in science, innovation, and health.
Elsevier is an equal opportunity employer. We considerqualified applicants for employment without regard to race, color, creed, religion, sex, national origin, citizenship status, disability status, veteran status, age, marital status, sexual orientation, gender identity, genetic information, orany other characteristic protected by law.
** alumnos must register separately to be considered for employment.**
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