Principal Data Scientist I

Overview

In this Principal Data Scientist role, you provide technical leadership across Data Science teams to shape AI, NLP, and generative AI solutions for knowledge discovery and decision support. You set architectural standards, governance, and quality practices, aligning efforts with Elsevier’s life sciences products Reaxys, Embase, and Pharmapendium. You guide architecture while remaining hands-on for high-priority work, ensuring production-ready, reusable patterns. You collaborate with senior stakeholders to translate business priorities into scalable technical direction, delivering impact across multiple teams.

Pay / Benefits

• wellbeing initiatives
• shared parental leave
• study assistance
• sabbaticals
• flexible working hours
• 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 with reusable patterns
• Review code and architecture for production readiness; contribute hands-on when needed
• Unify disparate approaches into shared libraries/frameworks to reduce duplication
• Define and maintain evaluation, monitoring, and governance standards for quality, trust, and reliability across teams
• Lead or contribute to AI governance, model risk, and regulatory adherence
• Conduct or oversee technical reviews and quality audits to identify gaps and drive remediation
• Mentor and coach multiple data science teams to raise capability and ensure high-quality delivery
• Harmonise tooling and delivery practices to balance uniformity with team autonomy
• Represent data science in senior governance meetings and translate business priorities into technical direction
• Collaborate across teams to convert complex challenges into practical solutions

Key requirements

• Significant experience in data science, AI, ML, NLP, information retrieval at senior/principal level
• Hands-on background building AI/ML, NLP, generative AI, and retrieval-based systems
• Expertise with LLMs including fine-tuning, prompt engineering, grounding strategies, and responsible AI
• Experience defining technical standards, evaluation frameworks, or governance across teams
• Working knowledge of AI governance, model risk, and compliance requirements
• Strong Python skills and solid ML fundamentals
• Experience with large-scale text/content-rich datasets and modern AI/ML frameworks
• Experience with RAG, semantic/vector/hybrid search, and experimentation measuring user impact
• Familiarity with cloud platforms and modern software engineering practices
• Excellent communication and stakeholder management; ability to present to senior/non-technical audiences
• Strong mentoring and leadership across multiple teams

• communication
• stakeholder management
• mentoring and leadership
• Python
• AI/ML
• NLP

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