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.
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.
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 – leading discovery, shaping data-driven use cases, and demonstrating how Elsevier’s offerings can be combined to power advanced analytics, knowledge graphs, and AI-enabled workflows, including grounding LLMs, agents, and RAG applications in trusted scientific data.
This role focuses mainly on pre-sales activities, with close collaboration across post-sales, professional services, and product teams to ensure proposed solutions are technically feasible, scalable, and aligned with real-world delivery and adoption.
Success in this role is built on a strong technical foundation, practical AI and data expertise, curiosity about scientific domains, 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 expertise in Elsevier’s solutions, including curated datasets, data APIs and integration options, MCP servers, AI agents, and SciBite semantic solutions, and how these combine to give customers' analytics and AI applications trusted access to Elsevier data and deliver 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-powered workflows and agents, 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.
- Stay current with developments in AI (e.g., LLMs, agentic workflows, and responsible AI) and advise customers on how to apply them with Elsevier data, while using AI tools to improve the speed and quality of pre-sales work such as demos, proposals, and PoCs.
Cross-functional alignment
- Validate solution feasibility in collaboration with Product 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.
Requirements
- Have deepp understanding of data-driven analytics workflows, including data ingestion, enrichment, integration, and insight generation
- Have working knowledge of Generative AI and LLM-based solutions (e.g., RAG, prompt design, agentic workflows, evaluation, and responsible AI and data governance), and of how NER, ontologies, taxonomies, and knowledge graphs are applied to real-world analytical use cases
- Have working knowledge of cloud and technical ecosystems (e.g., AWS, REST APIs, SFTP, HTTP, OAuth, Docker, Python, etc.), with familiarity with AI platforms, tooling, and integration patterns (e.g., LLM APIs, MCP).
- Have a degree in a science, technology, or data field (Master’s or PhD advantageous), or equivalent practical experience.
- Experience working in data-intensive enterprise environments (e.g., technology, engineering, pharma, or life sciences); life sciences experience is desirable but not essential, alongside a willingness to learn scientific domains.
- Have an understanding of how data and software are used to support research, analytics, and decision-making is highly valued.
- Experience delivering technical demonstrations and presentations to both technical and non-technical stakeholders
- Excellent communication, storytelling, and stakeholder-management skills.
- Display confidence leading complex technical discussions and navigating challenging or ambiguous customer conversations
- Able to travel to customer sites and industry events as required.
Work in a Way That Works for You
We promote a healthy work-life balance and offer flexible working options. Our benefits include wellbeing initiatives, shared parental leave, study assistance, and sabbaticals—supporting both your immediate responsibilities and long-term goals.
Working for You
We offer a comprehensive benefits package, including:
Health benefits: medical, dental, and vision coverage
Retirement benefits: 401(k) with match and Employee Share Purchase Plan
Wellbeing: wellness platform, Headspace subscription, and Employee Assistance Programs
Insurance: short- and long-term disability, life, accidental death, critical illness, and hospital indemnity
Family benefits: parental leave, adoption, and surrogacy support
Savings accounts: HSA, FSA, dependent care, and commuter benefits
Paid time off, plus volunteer and Employee Resource Group participation days
About the Business
As a global leader in information and analytics, we help researchers and healthcare professionals advance science and improve health outcomes. By combining trusted content with advanced analytics, we support research, education, and clinical practice. At Elsevier, your work contributes to solving global challenges and building a more sustainable future.
We know your well-being and happiness are key to a long and successful career. We are delighted to offer country specific benefits. Click here to access benefits specific to your location.
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We are an equal opportunity employer: qualified applicants are considered for and treated during employment without regard to race, color, creed, religion, sex, national origin, citizenship status, disability status, protected veteran status, age, marital status, sexual orientation, gender identity, genetic information, or any other characteristic protected by law.
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Skills Required
- Deep understanding of data-driven analytics workflows, including ingestion, enrichment, integration, and insight generation
- Working knowledge of generative AI and LLM solutions, including RAG, prompt design, agentic workflows, evaluation, responsible AI, and data governance
- Working knowledge of NER, ontologies, taxonomies, and knowledge graphs
- Working knowledge of cloud and technical ecosystems, including AWS, REST APIs, SFTP, HTTP, OAuth, Docker, and Python
- Familiarity with AI platforms, tooling, and integration patterns, including LLM APIs and MCP
- Degree in science, technology, or data, or equivalent practical experience
- Master's or PhD degree
- Experience in data-intensive enterprise environments such as technology, engineering, pharma, or life sciences
- Life sciences experience
- Understanding of how data and software support research, analytics, and decision-making
- Experience delivering technical demonstrations and presentations to technical and non-technical stakeholders
- Excellent communication, storytelling, and stakeholder-management skills
- Confidence leading complex technical discussions and navigating challenging or ambiguous customer conversations
- Ability to travel to customer sites and industry events as required
Elsevier Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Elsevier and has not been reviewed or approved by Elsevier.
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Leave & Time Off Breadth — Feedback suggests paid time off spans vacation, holidays, sick days, bereavement, military leave, and volunteer time. Family-related leave options are also emphasized as part of the package.
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Healthcare Strength — Feedback suggests medical, dental, vision, life insurance, wellness initiatives, and an EAP form a comprehensive health offering. Gym support and wellbeing hubs reinforce an ongoing health and wellness focus.
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Retirement Support — Feedback suggests retirement programs include a 401(k)/retirement plan and pension options, with long-term savings vehicles such as an employee stock purchase plan also available. These elements contribute to a sense of financial security beyond base pay.
Elsevier Insights
What We Do
Elsevier is a world-leading provider of information solutions that enhance the performance of science, health, and technology professionals, empowering them to make better decisions, and deliver better care. Because informed decisions lead to better outcomes, Elsevier is a leader in information and analytics for customers across the global research and health ecosystems. Elsevier helps researchers and healthcare professionals advance science and improve health outcomes for the benefit of society. We do this by facilitating insights and critical decision-making for customers across the global research and health ecosystems.
