Product Director - Evinova

AstraZeneca•Durham, NC

1d•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. This founding product leadership role will define the vision, strategy, and roadmap for an AI-native product intended to transform how clinical development and clinical operations teams work. The role prioritizes end-user needs, collaborative workflows, and connectivity across Evinova’s modules. The Product Director will operate with significant autonomy as the general manager for the product area and will be an individual contributor without direct reports. They will lead a matrixed team across engineering, design, data science, and commercial through influence and clarity. The role includes identifying and validating opportunities with users and customers, launching initial products, iterating based on evidence, and embedding AI and machine learning throughout product development and experience. The role owns the product lifecycle from concept and design through development, launch, go-to-market, and post-launch iteration. It also includes setting success metrics, owning the product area’s P&L outlook and resource trade-offs, and balancing speed to market with regulatory compliance, including GxP, 21 CFR Part 11, GDPR, and HIPAA. The position is based on an average expectation of at least three days per week in the office, with individual flexibility.

Requirements

At least 8 years of experience.
Bachelor’s degree.
Director-level or senior product leadership experience owning a product area end-to-end.
Experience in clinical trials or life sciences, with a deep understanding of clinical development and clinical operations personas and the clinical ecosystem, including EDC, CDMS, CTMS, and eTMF.
Ability to engage credibly with clinical operations leaders, medical directors, biostatisticians, data managers, and regulatory professionals.
Entrepreneurial track record, including taking at least one product from zero to one and demonstrating 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 product UX.
Experience operating in enterprise B2B SaaS environments, including complex customer landscapes, long sales cycles, and platform-level product decisions.
Technical skills to partner with engineers and data scientists, review technical specifications, and contribute to architectural trade-off decisions.
Daily use of AI tools for work such as research, analysis, writing, prototyping, and decision-making, and the ability to explain how AI is changing product development.
Ability to evaluate AI/ML model outputs, reason about precision and recall trade-offs, and make product decisions about thresholds, confidence scoring, and human-in-the-loop design.
Current understanding of foundation models, agentic AI, retrieval-augmented generation, and fine-tuning, with a point of view on their application in regulated 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 first-hand understanding of users, their problems, and their daily workflows, identifying opportunities for step-function impact.
Validate hypotheses rapidly through prototyping and experimentation.
Own the full product lifecycle, including concept, design, development, launch, go-to-market, and post-launch iteration using usage data and customer feedback.
Architect product capabilities around AI and machine learning, such as intelligent automation, contextual recommendations, predictive insights, and generative AI-powered workflows.
Collaborate with data science and engineering teams to turn AI capabilities into practical, regulation-aware product features.
Design intuitive, collaborative workflows that reduce user burden and connect clinical development and clinical operations teams across Evinova modules.
Support users through change management and adoption of AI-enabled ways of working.
Track the clinical trial and AI landscapes and identify relevant opportunities for emerging capabilities.
Partner with Evinova’s matrixed product organization to connect modules and inform the product roadmap.
Partner with commercial teams to avoid overfitting the product to a 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 commercial outcomes.
Balance speed to market with regulatory compliance requirements.
Cultivate partnerships with data providers and standards bodies, including CDISC and HL7 FHIR.
Champion experimentation, intellectual integrity, and continuous learning.