Associate Director, AI & Automation Catalyst

London Source: WhatJobs

See advert for pay

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