Executive Director, Head of Computational Discovery & Translation, Infectious Disease

AstraZeneca • Boston, MA

6d • $258,158 - $387,236 • Hybrid

About The Position

The Executive Director and Head of Computational Discovery & Translation, Infectious Disease will lead the creation and deployment of a next-generation, AI-enabled discovery and translational platform to transform how vaccines and immune therapies are designed, developed, and advanced to the clinic. This role will have end-to-end ownership of a transversal discovery engine spanning neoantigen, viral, and bacterial antigen discovery—establishing a rational, data-driven framework that integrates multimodal datasets (genomics, RNAseq, single-cell, proteomics, and clinical data), non-canonical antigen sources, iterative protein engineering, and clinically anchored biomarker strategies. The Executive Director will define and implement a unified AI and data strategy across Early Infectious Diseases, building scalable platform capabilities that enable rapid, high-confidence target discovery, antigen and mRNA design, and translational insight generation. This leader will partner closely with AstraZeneca’s Enterprise AI organization to embed advanced machine learning, agentic approaches, and data-driven decision frameworks into the core of R&D—while elevating AI fluency, upskilling teams, and driving a step-change in how science is conducted across the organization. The candidate will set the strategy, standards, and decision frameworks that enable multiple programs to leverage a shared, high-impact capability, rather than operating within isolated assets. By integrating discovery and translational sciences into a cohesive engine, the Executive Director will ensure that insights generated at the earliest stages directly inform clinical development, accelerating the path to meaningful patient impact. The Executive Director will lead a high-performing, multidisciplinary platform team and deliver impact through deep cross-functional partnerships. Key interfaces will include Enterprise AI, Internal Clinical Data Sciences hubs, External data hubs, Translational Medicine teams to ensure clinical relevance and biomarker integration, and Discovery Sciences to advance protein design and validation. The role will also engage selectively with leading academic and biotech partners to access emerging technologies and reshape the internal innovation ecosystem. Operating at the intersection of immunology, data science, and engineering, this leader will pioneer novel, out-of-the-box approaches to train the immune system—including next-generation strategies to target cancer and complex pathogens. In addition, the Executive Director will inspire exceptional talent, foster a culture of innovation and accountability, and influence senior stakeholders to align on a bold vision for AI-enabled transformation—ultimately redefining how vaccines and immunotherapies are discovered, developed, and delivered.

Requirements

• PhD in computational biology, computer science, biomedical engineering, biophysics, bioinformatics, AI/ML, structural biology, or related discipline.
• MSc in Computational, biochemistry or biological sciences.
• Bachelor degree in Computer science.
• 15+ years of relevant industry and/or translational research experience, incorporating software engineering, AI/ML and computational biology in drug R&D and/or biomedical research.
• Proven track record building and deploying AI, computational biology, or translational data science platforms in drug discovery and/or clinical development.
• Demonstrated experience leading AI/data strategy across multiple stages of R&D, ideally including at least in one area such as target discovery, molecular design, preclinical validation, biomarker discovery, patient stratification, and clinical development.
• Strong foundation in computational biology and multimodal biological data integration, including omics, single-cell, proteomics, sequence/structure, assay, foundation models and clinical data.
• Experience translating computational predictions into biological experiments, preclinical validation plans, biomarker strategies, or clinical development decisions.
• Experience with machine learning/deep learning for biological prediction, multimodal modeling, foundation models, scientific LLMs or agentic AI workflows, and software/data infrastructure.
• Proven ability to build, scale, and lead multidisciplinary AI/data science teams, including direct and matrix leadership.
• Experience establishing operating models, governance standards, decision frameworks, reproducible workflows, and communication approaches for high-impact scientific AI.
• Experience working in a cross-functional team in industry to maximize expertise and accelerate success
• Experience developing and implementing novel computational methods to support immune-directed R&D / discovery-to-clinic AI
• Experience supporting or leading interdisciplinary discovery or development programs
• Proven experience leading and managing junior colleagues
• Proven experience developing and controlling budgets
• Excellent written and verbal communication, business analysis, and consultancy skills

Nice To Haves

• Comfortable working with and leading lab and computational scientists across disciplines
• Experience in immunology, vaccines, infectious disease, oncology immunotherapy, immune-mediated disease, or related therapeutic areas.
• Proven track record of publishing relevant results and tools in peer-reviewed journals, conferences, and other scientific proceedings.
• Experience in life sciences and healthcare
• Experience in novel methods development and application
• Experience in a complex global organization
• Experience with structural biology, protein folding, protein-protein interactions, developability, protein engineering, or sequence-to-function modeling.

Responsibilities

• Define and execute the AI and data strategy for Early Infectious Disease, spanning discovery, molecular design, preclinical translation, multi-dimensional analysis, biomarker strategy, and early clinical development.
• Establish a discovery-to-clinic AI platform vision that connects target and antigen discovery, structural biology, construct design, functional immunology, translational biomarkers, and clinical decision-making.
• Partner with Early ID leadership, Enterprise AI, Discovery Sciences, Translational Medicine, Clinical Data Sciences, Clinical Development, Regulatory, Safety, CMC, and Oncology to align platform priorities with portfolio needs.
• Influence senior and executive stakeholders on AI-enabled R&D strategy, resource allocation, external partnerships, and portfolio-level decision frameworks.
• Develop build/partner/buy strategy for emerging AI, immunology, structural biology, foundation model, and agentic AI capabilities.
• Build and operationalize reusable AI-enabled platform capabilities for antigen, epitope, neoantigen, viral, bacterial, antibody, peptide/protein, mRNA, and immune-therapy discovery programs.
• Establish structure-aware and sequence-aware workflows for antigen and target prioritization, protein/peptide/antibody design, mRNA/construct optimization, developability assessment, and experimental prioritization.
• Develop multimodal AI approaches integrating genomics, transcriptomics, single-cell, proteomics, structural, immunological, preclinical, clinical, and external knowledge data.
• Create closed-loop learning systems linking computational prediction, assay design, wet-lab and preclinical validation, clinical readouts, and model refinement.
• Build translational AI capabilities for immune-correlate discovery, biomarker strategy, patient stratification, response prediction, and trial-enabling decisions.
• Define model validation standards, evidence packages, uncertainty communication, model cards, reproducible workflows, and decision frameworks for platform outputs.
• Ensure platform outputs are adopted by asset teams and used in governance, experimental planning, biomarker strategy, and clinical translation.
• Build, lead, and develop a high-performing multidisciplinary team of AI scientists, computational biologists, ML engineers, bioinformaticians, and matrixed scientific collaborators.
• Establish an operating model for prioritization, delivery, governance, stakeholder engagement, and cross-functional execution across multiple programs.
• Manage direct and matrixed contributors, budgets, external vendors, academic partnerships, and biotech/technology collaborations.
• Raise AI fluency across Early ID by coaching scientific teams on AI use cases, limitations, interpretation, validation, and adoption.
• Foster a culture of scientific rigor, experimental accountability, platform reuse, innovation, and high-quality execution.

Benefits

• qualified retirement program [401(k) plan]
• paid vacation and holidays
• paid leaves
• health benefits including medical, prescription drug, dental, and vision coverage

What This Job Offers

• Job Type: Full-time
• Career Level: Executive
• Education Level: Ph.D. or professional degree