Technical Consultant; Pre-Sales

Position: Technical Consultant (Pre-Sales)
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** About the 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 aTechnical Consultant (Pre-Sales), you will play a critical role in driving revenue growth by partnering with Sales to position Elsevier’sdata and software solutions, and analytics capabilitiesas 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.
** Responsibilities
* *** Pre-sales solution leadership
*** 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
** Cross-functional alignment
*** Validate solution feasibility in collaboration with Product, Post-Sales and
* 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
** Requirements
* *** Technical skills
*** Strong understanding ofdata-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.)
** Customer facing skills
*** 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
** Domain experience & education
*** 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 used to support research, analytics, and…