Head of Artificial Intelligence – ICC

• Strategic Leadership: Define and implement the end-to-end AI strategy across CTD and TIE, aligned to enterprise AI goals, with a clear roadmap for 2026–2027 and beyond.
• Portfolio Orchestration: Prioritize and deliver a focused slate of initiatives including agentic knowledge hubs, predictive modeling for cell therapy, in silico protein and binder design, TCR affinity maturation, CRISPR off-target safety, and next-generation analytics.
• Agentic AI Development: Build, test, and scale knowledge hub capabilities that enable collaborative analysis, rapid retrieval of institutional knowledge, and faster, better decisions.
• Predictive Modeling for Cell Therapy: Lead models that optimize CAR-T design and performance, reducing cycle times from hypothesis to validation and improving program selection.
• In Silico Protein and Binder Design: Deploy AI workflows that generate and refine binders and mature affinity, increasing hit quality and reducing experimental burden.
• CRISPR Safety and Risk: Implement sophisticated off-target workflows to improve safety assessments, strengthen study build, and de-risk pipelines.
• Workflow Automation: Automate research and analytics processes to streamline operations, reduce manual effort, and increase reproducibility across sites and teams.
• AI Upskilling and Culture: Orchestrate training that lifts foundational AI literacy and fosters an innovative, high-integrity culture where scientists and engineers co-create solutions.
• Collaborator Partnership: Build deep collaboration with enterprise AI, platform, and external partners to align standards, share knowledge, and improve resource leverage.
• Governance and Value Realization: Implement robust governance, regulatory compliance, and budget/resource management; institute critical metrics that quantify scientific and operational value.
• Communication and Influence: Translate sophisticated technical insights into clear narratives for executive and non-technical collaborators, shaping R&D strategy and investment decisions.

• Advanced degree (Master’s or PhD) in Computer Science, Engineering, Mathematics, or a related quantitative field.
• Demonstrated 10+ years of experience successfully leading high-performing AI teams and sophisticated AI programs, ideally in life sciences, technology, or R&D-driven environments.
• Strategic skill in shaping, scaling, and transforming AI activities for maximum business and scientific impact.
• Expertise in the development and deployment of AI/ML technologies, with proven outcomes in sophisticated, multi-stakeholder environments.
• Strong understanding of biology or R&D workflows preferred but not required; ability to translate between technical and scientific teams is essential.
• Outstanding organizational, communication, and collaborator engagement skills, including experience communicating/translating sophisticated technical findings and priorities to executive and non-technical partners.
• Proven experience building, mentoring, and scaling multi-disciplinary teams comprised of machine learning scientists, AI engineers, and data professionals, distributed across multiple locations and embedded in different R&D teams.
• Track record of encouraging a collaborative, innovative, and high-integrity team culture.

• Direct experience applying AI/ML to cell therapy, protein engineering, immunology, or related modalities.
• Demonstrated delivery of one or more: agentic knowledge hubs, CAR-T predictive models, in silico binder generation, TCR affinity maturation workflows, CRISPR off-target analyses, or computational mutagenesis.
• Familiarity with LLMs, knowledge graphs, MLOps, and cloud-native platforms; experience integrating these into enterprise environments.
• Experience with data governance, model risk management, and compliance practices relevant to R&D and regulated settings.
• Success managing multi-site teams and external ecosystems, including vendors, consortia, and academic collaborations.
• Portfolio management experience with clear KPI frameworks and budget ownership.

When we put unexpected teams in the same room, we unleash ambitious thinking with the power to encourage life-changing medicines. In-person working gives us the platform we need to connect, work at pace and challenge perceptions. That's why we work, on average, a minimum of three days per week from the office. But that doesn't mean we're not flexible. We balance the expectation of being in the office while respecting individual flexibility. Join us in our outstanding and ambitious world!