Associate Principal & COAA Scientific AI Lead - Patient-Centered Solutions and EU Job Reading area,England UK,IT/Tech

Responsibilities

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.

Requirements

Degree in life sciences, health outcomes, clinical research, public health, biomedical sciences, data science, product management, technology, or a related field.
Experience in…