Principal Data Scientist I

Overview

As Principal Data Scientist, you provide technical leadership across data science teams to design AI, NLP, and generative AI solutions for knowledge discovery and decision support. You set standards, quality, and governance while guiding architecture and delivery decisions, with hands-on input on high-priority work. You unify diverse approaches into reusable patterns and represent data science in senior forums. This role combines strategic influence with practical impact in life sciences data workflows at Elsevier.

Pay / Benefits

• flexible working hours
• wellbeing initiatives
• shared parental leave
• study assistance
• sabbaticals
• country-specific benefits

Responsibilities

• Set technical direction and architectural standards for AI/ML, NLP, and generative AI across teams, including LLM workflows, RAG, retrieval, ranking, and recommendations.
• Guide integration of scientific metadata, ontologies, taxonomies, and knowledge assets into AI workflows for consistency and reuse.
• Review code and architecture for production readiness; contribute hands-on when needed.
• Unify disparate approaches into shared patterns, libraries, or frameworks to reduce duplication.
• Define and maintain evaluation, monitoring, and governance frameworks ensuring trust and reliability.
• Lead or contribute to AI governance, model risk assessment, and regulatory adherence.
• Conduct or oversee technical reviews and quality audits, driving remediation.
• Mentor across teams, harmonise tooling, and promote high-quality delivery with autonomy.
• Represent data science in senior management, communicating strategy, risk, and progress to non-technical stakeholders.
• Translate business priorities into technical direction and collaborate to solve complex, ambiguous challenges.

Key requirements

• Significant senior/principal-level experience in data science, AI, ML, NLP, information retrieval, or related field.
• Hands-on background building AI/ML, NLP, generative AI, and retrieval-based systems with ability to dive into detail.
• Expertise with LLMs: fine-tuning, prompt engineering, grounding, responsible AI.
• Proven track record defining technical standards, evaluation frameworks, or governance across teams.
• Knowledge of AI governance, model risk, and compliance requirements.
• Strong Python skills and solid ML fundamentals.
• Experience with large-scale text or content-rich datasets and modern ML frameworks.
• Experience with RAG, semantic/vector/hybrid search and experiments measuring user impact.
• Familiarity with cloud platforms and modern software engineering practices.
• Excellent communication and stakeholder management; mentoring and cross-team coordination experience.

• strong communication
• stakeholder management
• leadership and mentoring
• AI/ML/NLP
• generative AI
• LLMs

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