Teams at Elsevier

Leads multiple distributed engineering teams supporting SciVal, a large-scale research analytics platform. Sets engineering strategy, architectural standards, and technology roadmaps across search, data, analytics, APIs, and platform integration. Develops engineering leaders, manages workforce planning and delivery, drives platform modernization, and establishes standards for quality, security, observability, resilience, and technical debt. Partners with Product, Data, Architecture, UX, Security, and customers to deliver AI-enabled, agent-ready analytics experiences.

Design and build ML, NLP, and generative AI systems for scientific discovery and knowledge extraction. Work with large heterogeneous scientific datasets to develop semantic search, entity extraction, QA, summarization, embeddings, and production-ready models. Implement model evaluation, fine-tuning, deployment, monitoring, drift detection, and scalable data pipelines while collaborating across engineering, product, UX, and research teams.

Design and deliver production-ready AI, NLP, and generative-AI solutions (including LLMs and RAG) for knowledge discovery. Build retrieval, ranking, recommendation, and decision-support systems, integrate scientific metadata and ontologies, establish evaluation and monitoring, write production Python code, and provide technical leadership and mentoring.