Core Responsibilities
Delivery Ownership
Own delivery execution across the portfolio (scope, schedule, quality, dependencies, release readiness, production health) for clinical experience and patient engagement products (eConsent, ePRO/eCOA, patient portals and apps) and study start-up capabilities (site activation, study build, CTMS/eTMF integration).
Run the standing delivery cadence of operational reviews, backlog grooming, planning cycles (e.g., PI Planning) and release governance as a single portfolio-wide rhythm, and apply product-management rigor to prioritization, scoring business value, technical debt, effort and risk explicitly and agreeing success measures before work starts.
Define and implement the portfolio metrics framework: release predictability, defect escape, production reliability, study build and site activation cycle time, and adoption by sites, study teams and patients. Instrument it where stakeholders can see it, not inside a backlog tool.
Identify and solve organizational and operational problems and technical challenges across the portfolio, from resourcing conflicts and scope ambiguity to cross-team dependencies, escalating only what falls outside established guidelines and doing so with a recommendation.
Technical Direction & Engineering Standards
Set and defend the technical direction of the portfolio; review architecture and design, challenge weak decisions, and own standards for APIs, data contracts, interoperability (CDISC, HL7/FHIR) and reuse across clinical platforms.
Own the architecture and design decisions for the portfolio's products, approve solution and integration designs, and run a design review cadence that catches problems before a release rather than after one.
Raise the engineering bar on test automation, CI/CD, observability and the deliberate reduction of technical debt.
Stay personally current: spend enough time in the code, the integrations and the production systems to judge an estimate, read a pull request and ask the questions only a practitioner asks.
AI & Agentic Transformation
Define and land a 12 to 24 month vision for AI and agentic capability in study start-up and clinical experience: protocol and document intelligence, automated study build, site and patient-facing assistants. Make the business case for it.
Find the use cases, prove them quickly at low cost, and take the ones that work through validation into production use with the evaluation, guardrails and human-in-the-loop design that requires.
Build the team's AI fluency, make AI-assisted engineering the normal way of working, and partner with business owners to redesign clinical process rather than automate around it.
