Technical Pre-Sales Consultant, Professional Services - SciBite

Elsevier•Philadelphia, PA

21h

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) operates in a regionally aligned role covering North America, EMEA, or APAC (primarily Japan), reports to the global Director of Technical Consulting (Pre-Sales), and partners closely with Sales, post-sales, and Professional Services teams. The role drives 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 across 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 focuses mainly on pre-sales, while collaborating with post-sales, Professional Services, and Product teams to ensure proposed solutions are feasible, scalable, and aligned with delivery and adoption. The consultant develops expertise in Elsevier’s curated datasets, data APIs and integration options, MCP servers, AI agents, and SciBite semantic solutions. They lead technical discovery; design and deliver tailored demonstrations; scope proposals; and support proof-of-concepts, technical evaluations, and pilots. They advise customers on applying current AI approaches with Elsevier data, articulate solution trade-offs and dependencies, and serve as a trusted technical advisor in account strategy and competitive positioning. They also validate feasibility with Product and Professional Services and share customer feedback and market insights with Product and Marketing. Elsevier promotes work-life balance and flexible working options. Benefits include wellbeing initiatives, shared parental leave, study assistance, sabbaticals, health and retirement benefits, insurance, family benefits, savings accounts, paid time off, and opportunities for volunteer and Employee Resource Group participation. The position’s listed U.S. national base pay range is $72,200–$120,200, with a total target cash range of $111,000–$184,800. Geographic differentials may apply; regional pay ranges are also listed for New York, New York City, Rochester, New Jersey, and Ohio. Pay mix between base and variable compensation varies by sales role.

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 apply 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, such as LLM APIs and MCP.
A 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

A Master’s degree or PhD.
Life sciences experience; it is desirable but not essential.
Understanding 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.
Lead technical discovery to understand customer objectives, analytical workflows, data landscapes, and success criteria, and translate challenges into data-driven use cases, solution architectures, and value narratives.
Design and deliver tailored solution 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.
Clearly articulate 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, 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.
Validate solution feasibility with Product and Professional Services teams to support 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.