Technical Consultant; Presales

Overview

Do you want to lead impactful presales engagements where your knowledge of AI, ontologies, and advanced analytics directly drives customer insight and business growth?

Are you a strong communicator with a technical background?

About our Team

This role sits within Elsevier’s Advanced Data Solutions team, which is responsible for selling and enabling customer implementations of Elsevier’s data and software solutions, with a particular focus on the Datasets and Sci Bite offerings.

Technical Consultants (Pre-Sales) report to the global Director of Technical Consulting (Pre-Sales) while operating in regionally aligned roles, with ownership across North America, EMEA, and APAC (primarily Japan). The team partners closely with Sales, post-sales, and Professional Services teams to support enterprise customer engagements.

Our solutions are already deployed within the world’s top 20 pharmaceutical companies, as well as organizations across other verticals where data-driven insight and advanced analytics are critical.

About the Role

As a Technical Consultant (Pre-Sales), you will play a critical role in driving revenue growth by partnering with Sales to position Elsevier’s data and software solutions, and analytics capabilities as enablers of customer insight and decision-making. You will act as the technical authority throughout the sales cycle – driving discovery, defining data use cases, and showcasing how Elsevier’s solutions power advanced analytics, knowledge graphs, and AI workflows.

Success in this role is built on three pillars – a strong technical foundation, deep domain understanding, and exceptional customer-facing skills – enabling you to translate data and software capabilities into compelling, outcome-driven solutions.

• Develop and maintain deep expertise in Elsevier’s solutions including datasets, semantic technology, ontology management and analytics capabilities, including how they combine to deliver customer insights
• Lead technical discovery to understand customer objectives, analytical workflows, data landscapes, and success criteria, translating business and scientific challenges into data-driven use cases, solution architectures, and value narratives
• Design and deliver tailored solution demonstrations showcasing how Elsevier data and software enable insights (e.g., discovery, analytics, AI/ML readiness, and knowledge graph construction)
• Scope and prepare solution proposals, and support proof of concepts (PoCs), technical evaluations, and pilots to validate solution fit and value, clearly articulating technical trade-offs, assumptions, and dependencies across data, software, and services
• Act as a trusted technical advisor throughout the sales cycle, supporting account strategy, solution positioning, and competitive differentiation
• Validate solution feasibility in collaboration with Product, Post-Sales and Professional Services teams to ensure smooth delivery, adoption, and account growth and expansion
• Capture customer feedback and market insights to inform product positioning, roadmap priorities, and go-to-market strategy in partnership with Product and Marketing teams

• Strong understanding of data-driven analytics workflows, including data ingestion, enrichment, integration, and insight generation
• Working knowledge of Generative AI, NLP, ontologies, taxonomies, and knowledge graphs, and how these are applied to real-world analytical use cases
• Deep understanding of cloud and technical ecosystems (e.g., AWS, REST APIs, SFTP, HTTP, OAuth, Docker, Python, etc.)
• Proven experience delivering technical demonstrations and presentations to both technical and non-technical stakeholders
• Excellent communication, storytelling, and stakeholder-management skills
• Confidence leading complex technical discussions and navigating challenging or ambiguous customer conversations
• Willingness to travel to customer sites and industry events as required
• Master’s or PhD in a science or technology field, or equivalent practical experience
• Experience working with pharmaceutical, life sciences, engineering, or other data-intensive enterprise environments
• Understanding of how data and software are…