Director, Search & AI Evaluation

Search & AI evaluation

Ready to lead a data science organisation that pushes the boundaries of what intelligent systems can achieve?

Do you thrive on shaping strategy, inspiring teams, and delivering solutions that create real‑world impact?

Director, Search & AI evaluation

Ready to lead a data science organisation that pushes the boundaries of what intelligent systems can achieve?

Do you thrive on shaping strategy, inspiring teams, and delivering solutions that create real‑world impact?



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. Our Search & Evaluation organization plays a critical role in shaping the future of AI-powered discovery by building intelligent retrieval, ranking, and evaluation systems that power trusted scientific and healthcare experiences.

The team is responsible for advancing search relevance, retrieval quality, experimentation frameworks, and AI evaluation capabilities across Elsevier’s next-generation AI platforms and products.

We combine expertise in information retrieval, machine learning, analytics, experimentation, and generative AI to improve how users discover, synthesize, and interact with scientific knowledge.

Our work spans semantic search, retrieval-augmented generation (RAG), ranking systems, LLM evaluation, online experimentation, and scalable evaluation infrastructure. We partner closely with Product, Engineering, UX Research, Knowledge Graph, and Applied AI teams to deliver measurable improvements in AI quality and user outcomes.

About

The Role

We are looking for a Director, Data Science – Search & Evaluation, to lead the strategic direction, technical vision, and organizational development of our Search & Evaluation function.

This role will focus on defining and scaling evaluation frameworks, search relevance methodologies, retrieval optimization strategies, and AI quality measurement systems across Elsevier’s AI-powered discovery experiences.

You will lead a multidisciplinary team of Senior Data Scientists and Data Analysts responsible for evaluating and improving search, ranking, retrieval, recommendation, and generative AI systems. You will help establish best practices for experimentation, evaluation rigor, and AI quality while influencing product strategy and technical direction across multiple initiatives.

This role is ideal for a leader with deep expertise in search relevance, information retrieval, experimentation, and AI evaluation who can combine technical depth, strategic thinking, organizational leadership, and strong cross-functional influence.



Key Responsibilities

Search, Retrieval & AI Quality Strategy

• Define and drive the long-term strategy for search relevance, retrieval evaluation, ranking optimization, and AI system quality.
• Lead initiatives focused on improving:
• Search relevance and ranking quality.
• Semantic retrieval and vector search
• Retrieval-augmented generation (RAG)
• AI grounding and hallucination mitigation
• User discovery and engagement outcomes
• Establish scalable evaluation methodologies for search, retrieval, recommendation, and LLM-powered systems.
• Guide experimentation and optimization across lexical, semantic, hybrid, and AI-assisted retrieval architectures.
• Partner with Product and Engineering leadership to align search and AI investments with customer and business priorities.
• Influence technical direction for retrieval systems, evaluation infrastructure, and AI quality frameworks across platforms.

• Define and operationalize evaluation frameworks for search and generative AI systems, including:
• IR metrics (e.g., NDCG, recall, precision)
• LLM and RAG evaluation methodologies
• Grounding and faithfulness evaluation
• Human evaluation and annotation strategies
• Online experimentation and A/B testing
• Establish best practices for offline benchmarking, online experimentation, and reproducible evaluation workflows.
• Build scalable processes for benchmark creation, annotation quality, evaluation governance, and performance reporting.
• Drive rigorous, evidence-based decision-making across…