Associate Principal & COAA Scientific AI Lead - Patient-Centered Solutions - UK and EU

• Own the product vision, roadmap, prioritisation, and lifecycle management for AI-enabled COA selection and COA Accelerator capabilities.
• Translate the strategic concepts into practical product capabilities, user journeys, workflows, release milestones, and measurable success criteria.
• Define the product's target users, use cases, workflows, outputs, quality standards, adoption needs, and commercial value proposition across internal and external use cases.
• Prioritise product capabilities across AI strategy generation, evidence retrieval, source citation, COA recommendation, endpoint selection, assessment schedule design, regulatory considerations, expert review, and report generation.
• Work with COA scientists, HEOR experts, strategy consultants, study teams, sponsors, commercial stakeholders, and clients to understand how COA selection is currently performed and where AI-enabled workflows can add value.
• Map current-state workflows for COA selection, internal expert support, prior-study benchmarking, regulatory precedent review, endpoint recommendation, and client-facing deliverables.
• Define product requirements for user inputs such as therapy area, indication, target product profile, mechanism of action, study phase, target population, endpoint objectives, geography, regulatory pathway, and intended use.
• Define expected product outputs, including recommended COAs, endpoint hierarchy, assessment schedule, rationale, evidence gaps, regulatory considerations, payer considerations, and source-backed recommendations.
• Ensure product requirements are specific enough for engineering delivery while preserving the nuance needed for expert scientific judgement.
• Partner with AI engineers and COA experts to define quality criteria for AI-generated outputs, including factuality, retrieval relevance, citation accuracy, clinical usefulness, consistency, and escalation triggers.
• Define when AI outputs can be used for internal decision support, when they require expert review, and when they may be suitable for client-facing use.
• Create workflows for expert review, feedback capture, error analysis, model improvement, quality monitoring, and continuous product refinement.
• Ensure the product clearly distinguishes between evidence-backed recommendations, expert inference, precedent-based assumptions, and areas of uncertainty.
• Partner with data engineering and domain experts to prioritise the knowledge sources required to power the product, including COA libraries, instrument metadata, psychometrics, clinical trial records, labels, HTA documents, regulatory correspondence, scientific literature, and internal consulting assets.
• Define metadata, traceability, citation, and source inspection requirements from a product and user perspective.
• Serve as a product-facing point of contact for internal stakeholders and selected client-facing discussions related to COA Strategy AI capabilities.
• Support demos, prototypes, pilots, business cases, training materials, internal enablement, onboarding workflows, and adoption planning.
• Work with Commercial and Strategy teams to define product positioning, packaging, differentiation, value story, and integration with broader COA Accelerator.
• Lead cross-functional delivery across AI Engineering, Data Engineering, Software Engineering, COA Science, UX, Security, Legal, Compliance, Commercial, and client-facing teams.
• Own backlog prioritisation, requirements definition, sprint planning input, acceptance criteria, release readiness, stakeholder communication, and product decision documentation.

• Degree in life sciences, health outcomes, clinical research, public health, biomedical sciences, data science, product management, technology, or a related field.
• Experience in COA strategy consulting, HEOR consulting, clinical outcomes research, eCOA, clinical trial technology, regulatory strategy, patient experience research, or medical evidence generation.
• Ability to translate complex workflows into product requirements, user journeys, and delivery plans.
• Strong stakeholder management skills, including ability to work with scientific experts, senior leaders, commercial teams, client-facing stakeholders, and technical contributors.
• Ability to balance scientific rigour, user needs, commercial value, technical feasibility, governance requirements, and delivery constraints.
• Strong written and verbal communication skills, including ability to create clear product narratives, requirements, executive materials, demos, and client-facing explanations.
• Strong prioritisation skills and ability to make pragmatic trade-off decisions in ambiguous, evolving, and regulated environments.
• Ability to work independently in a remote or hybrid environment while collaborating across global teams.
• Additional Requirements
• Experience with AI-enabled products, LLM applications, retrieval-augmented generation, knowledge management platforms, evidence synthesis tools, or clinical decision-support products is strongly preferred.
• Experience working with technical teams, including AI/ML engineers, data engineers, software engineers, UX designers, security teams, and platform teams.
• Experience in product management, AI product management, data product management, digital health, clinical technology, life sciences software, SaaS platforms, or technology enabled consulting offerings.
• Understanding of AI product risks, including overreliance, hallucination, weak source grounding, unclear confidence levels, and the need for expert review is preferred.
• Experience working with proprietary content, licensed data, regulated content, client-specific knowledge repositories, or permissioned knowledge spaces.
• Experience with agile delivery, backlog management, roadmap planning, cross-functional release management, and acceptance criteria definition.
• Experience or willingness to build products for both internal enterprise users and external client users.
• Ability to analyse usage data, client feedback, adoption metrics, and qualitative insights to inform product direction.
• Comfort with virtual collaboration, training, demo, and client engagement tools such as Teams, Zoom, PowerPoint, whiteboarding tools, and product management platforms.
• Significant experience leveraging AI tools for work.
• Fluency in English.

Please submit your CV in English. Please note we don't sponsor visa.

IQVIA is a leading global provider of clinical research services, commercial insights and healthcare intelligence to the life sciences and healthcare industries. We create intelligent connections to accelerate the development and commercialization of innovative medical treatments to help improve patient outcomes and population health worldwide. Learn more at https://jobs.iqvia.com

IQVIA is committed to integrity in our hiring process and maintains a zero tolerance policy for candidate fraud. All information and credentials submitted in your application must be truthful and complete. Any false statements, misrepresentations, or material omissions during the recruitment process will result in immediate disqualification of your application, or termination of employment if discovered later, in accordance with applicable law. We appreciate your honesty and professionalism.