Interested in building and shaping AI systems that combine machine learning, retrieval, and modern language models to solve real-world problems
About the team: You’ll work closely with engineering, product, and cross-functional partners to deliver robust, scalable solutions, while helping raise the technical bar across the team.
About the role: We’re looking for a Senior Data Scientist I to lead the design and development of intelligent, data-driven systems that help users explore, understand, and act on complex information.
In this role, you’ll take ownership of high-impact problem areas—driving solutions from concept to production while navigating ambiguity and making key technical decisions. You’ll combine strong machine learning foundations with modern AI approaches to build systems that integrate prediction, retrieval, and generation at scale.
What You’ll Do
- Lead the design and development of machine learning and AI-powered systems for complex, real-world problems
- Own end-to-end solutions—from problem framing and experimentation to production deployment and iteration
- Drive technical direction for key problem areas, making informed trade-offs between modeling approaches, system design, and scalability
- Apply a range of techniques—from classical ML to large language model (LLM) approaches—based on the problem context
- Design systems that combine retrieval, ranking, and generation to enable intelligent workflows
- Define and drive evaluation strategies, ensuring high-quality, reliable, and trustworthy outputs
- Contribute to system architecture and scalability, working closely with engineering teams
- Mentor and guide other data scientists, helping raise the overall technical bar of the team
- Influence product direction by translating technical insights into actionable recommendations
- Collaborate with product teams to design experiments, measure impact, and iterate based on evidence
What We’re Looking For
Core Qualifications
- Significant experience in data science, machine learning, or a related field
- Strong Python skills with a track record of building production-quality, maintainable systems
- Deep understanding of machine learning fundamentals and experience applying them in real-world settings
- Proven experience owning and delivering end-to-end data science or AI projects
- Strong problem-solving skills and ability to navigate ambiguity
- Ability to write clean, tested, well-documented code suitable for collaborative development
- Experience working with cross-functional teams to deliver user-facing data or AI products
AI & Modern Techniques
- Hands-on experience working with large language models (LLMs) in applied or production settings
- Experience with retrieval-based approaches (e.g., embeddings, semantic search, ranking)
- Experience building or contributing to LLM-powered systems involving multi-step reasoning, tool use, or workflow orchestration (e.g., agent-based approaches or frameworks such as LangChain, LangGraph, or similar)
- Strong understanding of when to apply classical machine learning vs. LLM-based approaches
Data & Engineering
- Experience working with large, complex, and unstructured datasets (especially text)
- Familiarity with ML frameworks (e.g., PyTorch, TensorFlow, Scikit-learn, HuggingFace)
- Experience with cloud platforms and modern development workflows (Git, CI/CD)
- Ability to contribute to scalable system design and collaborate effectively with engineering teams
- Experience with vector databases or search infrastructure such as Elasticsearch, OpenSearch, Qdrant or similar.
Collaboration & Leadership
- Excellent communication skills with the ability to influence both technical and non-technical stakeholders
- Ability to explain model behavior, trade-offs, and limitations clearly to non-technical audiences
- Experience mentoring or supporting other team members
- Ability to drive projects forward independently while aligning with broader team goals
Nice to Have
- Experience with search, information retrieval, or knowledge systems at scale
- Experience designing evaluation frameworks for AI-generated or retrieval-based outputs
- Exposure to MLOps tools and practices (e.g., MLflow, Kubeflow, SageMaker)
- Experience optimizing systems for performance, scalability, or cost
- Familiarity with frameworks or tools for orchestrating LLM-based workflows and agent-like systems
- Experience working in structured and reproducible development environments (e.g., clear specification of tasks, experiment tracking, and collaborative workflows)
- Interest in emerging paradigms such as spec-driven or AI-assisted development
- Experience with citation-aware, source-grounded, or evidence-based AI systems
Why Join Us
- Lead work on impactful problems involving complex, real-world data
- Build and shape systems that combine machine learning fundamentals with modern AI approaches
- Collaborate with a team focused on quality, scalability, and meaningful outcomes
- Opportunity to influence both technical direction and product evolution
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Skills Required
- Significant experience in data science, machine learning, or a related field
- Strong Python skills and experience building production-quality, maintainable systems
- Deep understanding of machine learning fundamentals and real-world application experience
- Experience owning and delivering end-to-end data science or AI projects
- Strong problem-solving skills and ability to navigate ambiguity
- Ability to write clean, tested, and well-documented collaborative code
- Experience working cross-functionally to deliver user-facing data or AI products
- Hands-on experience with large language models in applied or production settings
- Experience with retrieval-based approaches, embeddings, semantic search, or ranking
- Experience building LLM-powered systems involving multi-step reasoning, tool use, or workflow orchestration
- Strong understanding of when to apply classical machine learning versus LLM-based approaches
- Experience with large, complex, and unstructured datasets, especially text
- Familiarity with machine learning frameworks such as PyTorch, TensorFlow, Scikit-learn, or Hugging Face
- Experience with cloud platforms and modern development workflows including Git and CI/CD
- Ability to contribute to scalable system design and collaborate with engineering teams
- Experience with vector databases or search infrastructure such as Elasticsearch, OpenSearch, or Qdrant
- Excellent communication skills with technical and non-technical stakeholders
- Ability to explain model behavior, trade-offs, and limitations clearly
- Experience mentoring or supporting other team members
- Experience with search, information retrieval, or knowledge systems at scale
- Experience designing evaluation frameworks for AI-generated or retrieval-based outputs
- Exposure to MLOps tools such as MLflow, Kubeflow, or SageMaker
- Experience optimizing systems for performance, scalability, or cost
- Experience with LLM workflow orchestration or agent-like systems
- Experience with structured and reproducible development environments
- Experience with citation-aware, source-grounded, or evidence-based AI systems
Elsevier Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Elsevier and has not been reviewed or approved by Elsevier.
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Leave & Time Off Breadth — Feedback suggests paid time off spans vacation, holidays, sick days, bereavement, military leave, and volunteer time. Family-related leave options are also emphasized as part of the package.
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Healthcare Strength — Feedback suggests medical, dental, vision, life insurance, wellness initiatives, and an EAP form a comprehensive health offering. Gym support and wellbeing hubs reinforce an ongoing health and wellness focus.
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Retirement Support — Feedback suggests retirement programs include a 401(k)/retirement plan and pension options, with long-term savings vehicles such as an employee stock purchase plan also available. These elements contribute to a sense of financial security beyond base pay.
Elsevier Insights
What We Do
Elsevier is a world-leading provider of information solutions that enhance the performance of science, health, and technology professionals, empowering them to make better decisions, and deliver better care. Because informed decisions lead to better outcomes, Elsevier is a leader in information and analytics for customers across the global research and health ecosystems. Elsevier helps researchers and healthcare professionals advance science and improve health outcomes for the benefit of society. We do this by facilitating insights and critical decision-making for customers across the global research and health ecosystems.
