Principal Data Scientist

Data Science Life Sciences is a diverse team focusing on GenAI, ML, NLP. We mainly develop best-in-class enrichment pipelines for Elsevier’s life science .com products such as Reaxys, Embase and Pharmapendium.

As Principal Data Scientist (AI/NLP & Generative AI), you will provide technical and thought leadership across multiple data science teams, shaping how we design and deliver advanced AI, NLP, and generative AI solutions for knowledge discovery and decision support. Your focus is on setting technical standards, quality, and governance across teams, representing data science in senior forums, and staying close enough to the work to guide architecture and delivery decisions.

Hands on involvement will be occasional, on high priority or high risk work.

• Set technical direction and architectural standards for AI/ML, NLP, and generative AI solutions across teams, including LLM powered workflows, RAG systems, retrieval, ranking, and recommendation capabilities.
• Guide how scientific metadata, ontologies, taxonomies, and knowledge assets are integrated into AI workflows, favouring reusable, consistent patterns over one off implementations.
• Review code, architecture, and technical decisions across teams to ensure production readiness, stepping in hands on when needed.
• Unify disparate technical approaches into shared patterns, libraries, or frameworks, reducing duplication and divergence across teams.
• Define and maintain shared standards, evaluation, and monitoring frameworks that ensure consistent quality, trust, and reliability across all teams.
• Lead or contribute to AI governance and compliance activities, including responsible AI practices, model risk assessment, and regulatory adherence.
• Conduct or oversee technical reviews and quality audits, identifying gaps and driving remediation.
• Mentor and coach across multiple data science teams to raise capability and support consistent, high quality delivery.
• Bring uniformity and agility to distributed teams by harmonising tooling and delivery practices without stifling team level autonomy.
• Represent the data science function in senior management and governance meetings, communicating strategy, risk, and progress to non technical stakeholders.
• Act as a trusted technical advisor to senior leadership, translating business priorities into technical direction.
• Collaborate across teams and functions to turn complex, ambiguous challenges into practical solutions.

• Significant experience in data science, AI, machine learning, NLP, information retrieval, or a related field, including operating at a senior or principal level.
• Strong hands on background building AI/ML, NLP, generative AI, and retrieval based systems, with the ability to step into technical detail when required.
• Expertise with LLMs, including fine tuning, prompt engineering, grounding strategies, and responsible AI practices.
• Demonstrated experience defining technical standards, evaluation frameworks, or governance processes across teams, not just within a single project.
• Working knowledge of AI governance, model risk, and compliance requirements.
• Strong Python skills and solid machine learning fundamentals.
• Experience with large scale text or content rich datasets and modern AI/ML frameworks.
• Experience with RAG, semantic, vector, or hybrid search, and experimentation approaches that measure user impact.
• Familiarity with cloud platforms and modern software engineering practices.
• Excellent communication and stakeholder management skills, with proven ability to represent technical work to senior and non technical audiences.
• Strong mentoring and leadership skills, with experience influencing and coordinating across multiple teams.