Director, AI and Machine Learning

Key Responsibilities

• Set the strategy for shared AI and machine learning capabilities
• Define and execute the group's AI/ML strategy across therapeutic areas, prioritizing reusable capabilities that address clinical development questions and regulatory evidence needs
• Translate scientific priorities into a coordinated roadmap for methods, platforms, and analytical products
• Partner with therapeutic area computational biology leaders and program-facing scientists to integrate shared capabilities into development workflows
• Balance innovation, delivery, adoption, resource needs, and operating costs
• Influence Precision Medicine AI/ML strategy
• Own the scientific analytics stack and infrastructure roadmap
• Lead the architecture and evolution of the group's analytics stack, spanning data preparation and integration, computational workflows, model development, evaluation, deployment, and scientific interpretation in clinical development settings
• Partner with enterprise technology, data engineering, security, and infrastructure teams to deliver scalable AWS-based computing and AI capabilities aligned with enterprise architecture and governance
• Establish reusable software, workflows, and standards for reproducibility, version control, testing, documentation, data lineage, and ongoing platform and model operations
• Advance clinical and translational analytics
• Lead the development and application of statistical, machine learning, deep learning, and other AI methods to integrate multi-omics, imaging, biomarker, and clinical data from human studies
• Develop reusable approaches for patient stratification, response prediction, pharmacodynamic analysis, and mechanism-of-action studies, linking biomarker and imaging findings to clinical endpoints, efficacy, and safety
• Direct cross-study and cross-asset analyses that support translation and reverse translation, using biological hypotheses to inform clinical biomarker strategies and clinical observations to generate mechanistic hypotheses for experimental testing and future studies
• Anchor analytical strategies in clinical trial design, endpoints, patient populations, assay limitations, confounding, missing data, and uncertainty
• Apply validation appropriate to the scientific question, clinical decision, and intended evidentiary use
• Guide human data strategy and evidence gap analysis
• Identify and prioritize evidence gaps across clinical programs and therapeutic areas, and determine where additional human datasets could strengthen biomarker interpretation, predictive models, and clinical or regulatory evidence generation
• Lead scientific and analytical evaluation of external human datasets, recommending which to acquire, license, and internalize based on relevance to priority questions, population and outcome coverage, data quality, reuse potential, permitted uses, and integration requirements
• Agentify existing workflows and operationalize AI
• Lead agentification of existing analytical workflows, redesigning repeatable steps with AI assistants and coordinated agents to increase operational productivity and reduce routine manual quality-control effort
• Automate routine validation, consistency checks, and QC triage, directing exceptions and scientific interpretation to appropriate experts with traceable review decisions
• Guide the use of Codex and other enterprise-approved AI tools to develop and orchestrate agent-enabled workflows connecting scientific data, analytical tools, models, and expert review
• Establish evaluation and monitoring approaches for scientific accuracy, reliability, reproducibility, appropriate tool use, and human oversight
• Support clinical and regulatory evidence generation
• Embed data privacy, access controls, provenance, model documentation, and fit-for-purpose quality practices into shared capabilities used with sensitive human data and clinical analytical workflows
• Partner with biostatistics, clinical development, precision medicine, regulatory, safety, medical, and quality colleagues to align analysis plans with clinical and regulatory evidence needs and document assumptions, limitations, and interpretation
• Enable traceable, scientifically reviewed analyses for clinical development decisions, regulatory submissions and health authority questions, publications, and medical and scientific communications, with evidence standards appropriate to each intended use
• Lead people and evolve how the group works
• Manage, coach, and develop a scientific and technical team
• Set clear priorities and performance expectations and build an inclusive culture of scientific rigor, collaboration, and accountability
• Strengthen AI fluency across computational biologists, bioinformaticians, statisticians, mathematicians, data scientists, machine learning scientists, and engineers through practical demonstrations, training, and collaboration
• Lead adoption of new tools and working practices
• Track improvements in analytical turnaround time, manual QC effort, reuse, adoption, scientific quality, and decision value
• Communicate strategy, results, investment needs, and trade-offs clearly to scientific, technical, and executive audiences