Senior Director, Enterprise Architecture — Research & Development

Scorpion Therapeutics; Pennsylvania; On-site; USD 210,000 - 260,000; Full time

Job summary

Scorpion Therapeutics is seeking a senior leader to align R&D priorities with enterprise technology strategy and governance. You will partner with R&D senior leadership and the Architecture Leadership Team to connect divisional priorities to platforms, standards, and investments.

This role drives AI acceleration, evaluates emerging technologies, and guides roadmaps while ensuring risk, compliance, and cross-functional collaboration across pharma workflows.

Qualifications

• Deep end-to-end pharmaceutical R&D domain knowledge; familiarity with 21 CFR Part 11, GxP, CSV.
• Strong AI/ML and generative AI acumen; bridge data scientists, business leaders, and technology teams; responsible AI.
• Strategic/enterprise thinking; influencing without authority; navigating matrix organizations.
• Exceptional communication and evidence-based decision-making.
• Fluency in Cloud, SaaS, data platforms, and modern integration; evaluate vendors using fit/risk/scalability/TCO.

Responsibilities

• Develop trusted relationships with R&D senior leadership and the Architecture Leadership Team to connect divisional priorities to enterprise capabilities, platforms, standards, investments, and technology direction.
• Advise on enterprise technology opportunities, dependencies, risks, tradeoffs, and cross-divisional implications.
• Help R&D leaders accelerate objectives using enterprise capabilities and shared platforms while reducing duplication, risk, and complexity.
• Bring visibility to enterprise implications and cross-divisional dependencies early to inform planning and investment discussions.
• Represent R&D priorities, demand signals, and capability needs in enterprise technology and architecture discussions.
• Engage R&D leadership early across discovery, lab operations, preclinical, clinical development, regulatory, pharmacovigilance, and clinical supplies manufacturing.
• Drive AI acceleration: identify high-value use cases, prioritize by business impact, and ensure strategic alignment.
• Evaluate emerging technologies (AI/ML, generative AI, Cloud/SaaS, advanced analytics, modern data integration) and translate them into enterprise strategies.
• Apply total-cost-of-ownership and business-outcomes lens to investments.
• Own quality/traceability of enterprise architecture decisions affecting R&D; ensure rationale and outcomes are visible and reusable.
• Translate decisions into standards, patterns, reference architectures, self-service guidance, and responsible AI guardrails.
• Optimize through reuse/standardization/simplification; quantify value and translate opportunities into roadmaps with owners.
• Bring future demand and market signals into enterprise technology foresight; maintain pharma/life sciences technology network; translate external insights into practical recommendations.
• Lead R&D Enterprise Architecture engagement; establish priorities, accountability, measurable outcomes; develop/coach EA talent and build succession depth.

Skills

AI/ML acumen

Enterprise architecture

Strategic thinking

Communication

Cloud platforms

GxP knowledge

Education

Bachelor's degree in CS/Engineering

Master's degree in Information Science/CS/Life Sciences

Job description

Responsibilities

• Develop trusted relationships with R&D senior leadership and the Architecture Leadership Team to connect divisional priorities to enterprise capabilities, platforms, standards, investments, and technology direction.
• Advise on enterprise technology opportunities, dependencies, risks, tradeoffs, and cross-divisional implications.
• Help R&D leaders accelerate objectives using enterprise capabilities and shared platforms while reducing duplication, risk, and complexity.
• Bring visibility to enterprise implications and cross-divisional dependencies early to inform planning and investment discussions.
• Represent R&D priorities, demand signals, and capability needs in enterprise technology and architecture discussions.
• Engage R&D leadership early across discovery, lab operations, preclinical, clinical development, regulatory, pharmacovigilance, and clinical supplies manufacturing.
• Drive AI acceleration: identify high-value use cases, prioritize by business impact, and ensure strategic alignment.
• Evaluate emerging technologies (AI/ML, generative AI, Cloud/SaaS, advanced analytics, modern data integration) and translate them into enterprise strategies.
• Apply total-cost-of-ownership and business-outcomes lens to investments.
• Own quality/traceability of enterprise architecture decisions affecting R&D; ensure rationale and outcomes are visible and reusable.
• Translate decisions into standards, patterns, reference architectures, self-service guidance, and responsible AI guardrails.
• Optimize through reuse/standardization/simplification; quantify value and translate opportunities into roadmaps with owners.
• Bring future demand and market signals into enterprise technology foresight; maintain pharma/life sciences technology network; translate external insights into practical recommendations.
• Lead R&D Enterprise Architecture engagement; establish priorities, accountability, measurable outcomes; develop/coach EA talent and build succession depth.

Qualifications

• Bachelor’s degree in Computer Science, Engineering, Information Security, or related field.
• 15+ years in technology strategy, enterprise architecture, digital transformation, or related leadership roles; ideally with pharmaceutical/biotech/life sciences R&D exposure.
• Self-directed leader who identifies needs and drives results with minimal direction.
• Demonstrated ability to lead teams, develop talent, and build succession depth.
• Bachelor’s or Master’s degree in Information Science, Computer Science, Bioinformatics, Life Sciences, or related field.

Required/Preferred Skills (selected)

• Deep end-to-end pharmaceutical R&D domain knowledge; understanding of 21 CFR Part 11, GxP, CSV.
• Strong AI/ML and generative AI acumen; bridge data scientists, business leaders, and technology teams; responsible AI (fairness, transparency, compliance).
• Strategic/enterprise thinking; influencing without authority; navigating matrix organizations.
• Exceptional communication and evidence-based decision-making.
• Fluency in Cloud, SaaS, data platforms, and modern integration; evaluate vendors using fit/risk/scalability/TCO.