Associate Director, AI & Automation Catalyst

Overview

In this role you drive the translation of AI and automation into production, shaping adoption roadmaps across cross-functional teams. You collaborate between AI/Data Engineering, SCE platform teams and IT to deliver measurable value while ensuring compliance. You lead change efforts to move AI-enabled processes from pilots to standard practice in a regulated environment. This is a hands-on, change-oriented role that balances technical credibility with stakeholder influence. You’ll join a mission to accelerate clinical decisions and bring innovative medicines to patients faster.

Pay / Benefits

• base salary + bonus potential
• insurance plans
• retirement plans
• wellbeing resources
• global recognition programs
• flexible and hybrid working options

Responsibilities

• Lead AI/automation production adoption and managed rollout from current state to scaled implementation
• Translate needs into clear requirements, user stories and acceptance criteria with cross-functional partners
• Balance build vs. configure decisions and human-in-the-loop validation
• Manage stakeholders and influence without authority across matrix teams
• Redesign QC/review workflows and update SOPs, RACI, and roles
• Design and execute change plans with training, champion networks and feedback loops
• Ensure GxP, computerised system validation and data integrity for AI steps
• Prototype and review code; provide expert guidance on complex data and automation problems
• Stay current with industry trends and share best practices for AI adoption in regulated environments

Key requirements

• MSc or PhD in Computer Science, Data Science, AI/ML, Biostatistics, Bioinformatics or another quantitative discipline
• Extensive technology delivery experience in a regulated industry with at least 3 years leading tech-enabled change
• Track record of taking AI/automation or digital solutions from pilot to scaled adoption with measurable impact
• Knowledge of clinical data flows and statistical programming (SDTM/ADaM, TFLs, QC) and GxP/CSV
• Strong reading/writing in R and/or Python; understanding of software engineering practices (VCS, testing, CI/CD, release management)
• Practical understanding of LLM/agentic AI, with risk, human-in-the-loop design and evaluation
• Excellent stakeholder management and influence at all levels, including senior leadership
• Fluent English, able to tell evidence-based stories to technical and non-technical audiences

• Stakeholder engagement
• Change management
• Curious mindset
• R or Python
• CI/CD and software engineering practices
• Statistical computing environments

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