Product Director - Evinova

AstraZeneca•Durham, NC

3d•Hybrid

About The Position

Evinova, a distinct business within the AstraZeneca group, is seeking an entrepreneurial, AI-fluent Product Director to own and build a core product area from the ground up. The role will define the vision, strategy, and multi-year roadmap for an AI-native product that improves how clinical development and clinical operations teams work. It focuses on designing intuitive, collaborative workflows for end users and connecting Evinova’s modules into a cohesive platform. This is a founding product leadership and individual-contributor role with significant autonomy. The Product Director will act as the general manager for the product area, leading a matrixed team across engineering, design, data science, and commercial through influence rather than direct reports. The role spans opportunity discovery, user and customer validation, prototyping, product development, launch, go-to-market, and ongoing iteration informed by data and feedback. The role combines AI-native product development with strategic and commercial ownership. The Product Director will translate AI and machine-learning capabilities into practical, regulation-aware features; align teams around a unified product vision; communicate with executive leaders and enterprise customers; define success metrics; manage resource trade-offs and the product’s P&L outlook; and balance speed with regulatory compliance. The position is based on an average expectation of at least three days per week in the office, with individual flexibility respected.

Requirements

At least 8 years of experience.
Bachelor’s degree.
Experience in a director-level or senior product leadership capacity, with end-to-end ownership of a product area.
Domain experience in clinical trials or life sciences, including understanding clinical development and clinical operations personas and the clinical ecosystem (EDC, CDMS, CTMS, and eTMF).
Ability to engage credibly with clinical operations leaders, medical directors, biostatisticians, data managers, and regulatory professionals.
Entrepreneurial track record taking at least one product from zero to one, as a founder, early-stage product manager, or intrapreneur; able to identify products conceived, shipped, and scaled.
Hands-on experience building AI/ML-powered products, including understanding model development lifecycles, training data strategies, prompt engineering, evaluation frameworks, and AI feature user experience.
Experience operating in enterprise B2B SaaS environments, including complex customer landscapes, long sales cycles, and platform-level product decisions.
Strong technical skills, including the ability to work directly with engineers and data scientists, review technical specifications, and contribute to architectural trade-offs.
Daily use of AI tools for work such as research, analysis, writing, prototyping, and decision-making, and ability to explain AI’s effects on product development.
Ability to evaluate AI/ML model outputs, reason about precision/recall trade-offs, and make product decisions involving model thresholds, confidence scoring, and human-in-the-loop design.
Current, informed perspective on foundation models, agentic AI, retrieval-augmented generation, and fine-tuning, with a point of view on their application in regulated industries such as clinical research.

Nice To Haves

Experience as a founder or co-founder of a technology company, or as the first product hire at an early-stage startup.
Working knowledge of clinical trial data standards, including CDISC SDTM/ADaM and CDASH, and regulatory submission workflows.

Responsibilities

Define and own the product vision, strategy, and multi-year roadmap for an AI-native product, from initial concept through scale.
Develop a deep understanding of users, their problems, and day-to-day workflows, identifying opportunities for substantial impact.
Validate hypotheses rapidly through prototyping and experimentation.
Own the end-to-end product lifecycle, including concept, design, development, launch, go-to-market, and post-launch iteration using usage data and customer feedback.
Architect the product around AI and machine learning, including capabilities such as intelligent automation, contextual recommendations, predictive insights, and generative AI workflows.
Partner with data science and engineering teams to turn AI capabilities into practical, regulation-aware product features.
Design intuitive, collaborative, end-user-first workflows that reduce burden and connect clinical development and clinical operations teams across Evinova modules.
Support users through change management and encourage adoption of AI-enabled workflows.
Monitor developments in clinical trials and AI, identifying opportunities to apply emerging capabilities to user challenges.
Partner with the matrixed product organization to improve connectivity across Evinova modules and inform the product roadmap.
Work with commercial teams to avoid overfitting the product to any single customer and iterate toward product-market fit.
Align engineering, design, data science, and commercial teams around a unified product vision.
Communicate product strategy, progress, and trade-offs to executive leadership and enterprise customers.
Define and own success metrics, including adoption, engagement, revenue contribution, and clinical trial cycle-time reduction, and use data to prioritize work.
Own the product area as a business, including its P&L outlook, resource trade-offs, and measurable commercial outcomes.
Balance speed to market with regulatory compliance, including GxP, 21 CFR Part 11, GDPR, and HIPAA.
Develop strategic partnerships with data providers and standards bodies, including CDISC and HL7 FHIR.
Champion experimentation, intellectual integrity, and continuous learning within the product area and across Evinova.