Associate Principal Scientist, Immunology Cell Therapy Discovery

AstraZeneca•Waltham, MA

8d•$134,893 - $202,339

About The Position

AstraZeneca is seeking a dedicated, hands-on AI expert (Associate Principal Scientist or Principal Scientist level) to join our Immunology Cell Therapy Discovery team. This role will provide scientific and technical leadership across discovery initiatives with a dual emphasis on immune mediated diseases and computational innovation, with particular focus on bioinformatics, data science, and AI architecture to accelerate cell therapy discovery. The successful candidate will operate at the interface of immunology, computational biology, and AI-enabled research, partnering across multidisciplinary teams to advance target identification, patient stratification, translational insights, and platform capabilities that support AstraZeneca’s discovery portfolio.

Requirements

PhD, MS, or BS with 5+ years of relevant biotech or biopharma industry experience in Immunology, Computational Biology, Bioinformatics, Molecular Biology, Cell Biology, Bioengineering, Data Science, Machine Learning or a related discipline.
Proven ability as an AI-enabled coder to design, develop, and maintain reusable codebases, contribute to or build AI-powered applications, and integrate AI solutions into scientific workflows to improve efficiency and innovation.
Strong programming proficiency in Python and/or R, with experience in workflow reproducibility, version control, modular code development, and scalable analytical pipelines.
Demonstrated expertise in bulk RNA-seq, single-cell RNA-seq, immune repertoire analytics, integrative multi-omics, and biomarker/endotype discovery, with a strong understanding of analytical design, quality control, and interpretation in a discovery setting.
Strong experience in statistical modeling, machine learning, data integration, and data visualization, with the ability to extract meaningful and decision-relevant insights from complex biological and translational datasets.

Nice To Haves

Experience building internal scientific tools, decision-support systems, workflow automation tools, or other AI-powered applications that enhance research productivity and discovery quality.
Established connections within immunology, computational biology, data science, or AI communities, including experience collaborating with CROs, academic groups, or research consortia.
Strong track record of peer-reviewed publications in immunology, computational biology, bioinformatics, data science, or related fields.
Experience working across multiple therapeutic modalities, including cell therapies, biologics, and/or small molecules, and familiarity with combination strategies relevant to autoimmune and allergic indications.

Responsibilities

Serve as an AI Architect within the discovery organization, acting as an expert in AI-enabled coding who can design, develop, and maintain reusable codebases that improve productivity, scalability, and scientific innovation.
Lead innovation by integrating AI into core workflows, contribute to or build AI-powered applications, and help drive transformation by advancing AI-driven discovery.
Architect practical AI solutions for scientific workflows, enabling automation, improving knowledge extraction, and establishing sustainable coding and software practices that enhance research efficiency and impact.
Lead and apply advanced bioinformatics approaches to support research in immune mediated diseases.
Design and implement robust analytical workflows for bulk and single-cell RNA sequencing, immune repertoire analysis, multi-omics integration, target and pathway identification, and biomarker or endotype discovery.
Drive data science strategies that integrate complex biological datasets to improve understanding of disease heterogeneity, preclinical readout, and mechanism of action.
Develop and apply statistical models, machine learning approaches, and advanced data integration methods to support hypothesis generation, patient segmentation, and evidence-based decision-making.
Contribute to building scalable and interpretable analytical frameworks that connect computational findings with experimental validation.
Design, develop, and maintain reusable AI-enabled codebases and computational frameworks.
Integrate AI into core discovery workflows.
Contribute to or build AI-powered applications.
Advance the use of AI to improve scientific decision-making and discovery efficiency.
Design, implement, and continuously improve analytical workflows for bulk and single-cell transcriptomics, immune repertoire analyses, multi-omics integration, and biomarker/endotype discovery, ensuring reproducibility, quality control, and scientific rigor.
Apply advanced data science and machine learning methods to derive insights from high-dimensional biological and translational datasets, supporting target selection, patient stratification, mechanism-of-action studies, and portfolio prioritization.
Build strong partnerships with key stakeholders internally and externally to align scientific, computational, and AI strategies with broader R&D priorities.
Communicate complex findings clearly to both technical and non-technical audiences.