Technical Pre-Sales Consultant, Professional Services - SciBite
About The Position
This role is part of Elsevier’s Advanced Data Solutions team, which sells and enables customer implementations of Elsevier data and software solutions. The Technical Consultant (Pre-Sales) works in a regionally aligned role across North America, EMEA, or APAC (primarily Japan), reports to the global Director of Technical Consulting (Pre-Sales), and partners with Sales, post-sales, and Professional Services teams. The role supports revenue growth by helping Sales position Elsevier’s data, software, and analytics capabilities as tools for customer insight and decision-making. As the technical authority throughout the sales cycle, the consultant leads discovery, develops data-driven use cases, and demonstrates how Elsevier offerings can support advanced analytics, knowledge graphs, and AI-enabled workflows—including grounding LLMs, agents, and RAG applications in trusted scientific data. The work is primarily pre-sales, with close collaboration with post-sales, Professional Services, and Product teams to ensure proposed solutions are feasible, scalable, and aligned with delivery and adoption. Success requires a strong technical foundation, practical AI and data expertise, curiosity about scientific domains, and excellent customer-facing skills. The position requires travel to customer sites and industry events as needed. Elsevier describes its work as combining trusted content with advanced analytics to support research, education, and clinical practice and contribute to a more sustainable future.
Requirements
- Deep understanding of data-driven analytics workflows, including data ingestion, enrichment, integration, and insight generation.
- Working knowledge of generative AI and LLM-based solutions, including RAG, prompt design, agentic workflows, evaluation, responsible AI, and data governance.
- Working knowledge of how NER, ontologies, taxonomies, and knowledge graphs are applied to real-world analytical use cases.
- Working knowledge of cloud and technical ecosystems such as AWS, REST APIs, SFTP, HTTP, OAuth, Docker, and Python.
- Familiarity with AI platforms, tooling, and integration patterns, including LLM APIs and MCP.
- Degree in a science, technology, or data field, or equivalent practical experience.
- Experience working in data-intensive enterprise environments, such as technology, engineering, pharma, or life sciences.
- 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.
Nice To Haves
- Master’s or PhD degree.
- Life sciences experience; described as desirable but not essential.
- Understanding of how data and software support research, analytics, and decision-making.
Responsibilities
- Develop and maintain expertise in Elsevier solutions, including curated datasets, data APIs and integration options, MCP servers, AI agents, and SciBite semantic solutions, and explain how these offerings support customer analytics and AI applications.
- Lead technical discovery to understand customer objectives, analytical workflows, data landscapes, and success criteria; translate business and scientific challenges into data-driven use cases, solution architectures, and value narratives.
- Design and deliver tailored demonstrations showing how Elsevier data and software enable discovery, analytics, AI-powered workflows and agents, and knowledge graph construction.
- Scope and prepare solution proposals and support proof-of-concepts, technical evaluations, and pilots to validate solution fit and value; communicate technical trade-offs, assumptions, and dependencies across data, software, and services.
- Serve as a trusted technical advisor throughout the sales cycle, supporting account strategy, solution positioning, and competitive differentiation.
- Stay current with AI developments, including LLMs, agentic workflows, and responsible AI, and advise customers on applying them with Elsevier data.
- Use AI tools to improve the speed and quality of pre-sales work, including demonstrations, proposals, and proof-of-concepts.
- Collaborate with Product and Professional Services teams to validate solution feasibility and support smooth delivery, adoption, account growth, and expansion.
- Capture customer feedback and market insights with Product and Marketing teams to inform product positioning, roadmap priorities, and go-to-market strategy.
Benefits
- Flexible working options.
- Wellbeing initiatives and a wellness platform.
- Shared parental leave and parental leave.
- Study assistance.
- Sabbaticals.
- Medical, dental, and vision coverage.
- 401(k) with match.
- Employee Share Purchase Plan.
- Headspace subscription and Employee Assistance Programs.
- Short- and long-term disability, life, accidental death, critical illness, and hospital indemnity insurance.
- Adoption and surrogacy support.
- HSA, FSA, dependent care, and commuter benefits.
- Paid time off.
- Volunteer and Employee Resource Group participation days.
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What This Job Offers
Job Type
Full-time
Education Level
No Education Listed
