Director, Immuno-oncology Bioinformatics

AstraZeneca•Waltham, MA

2d•$181,000 - $272,000

About The Position

The Translational Medicine (TM) Early Data Science (EDS) group delivers data-driven, actionable insights through the application of computational science to AstraZeneca's Oncology portfolio. The group has substantial expertise and a renowned track record in bioinformatics and data science, undertaking both portfolios applied informatics research and the development of cutting-edge capabilities. Within the IO Bispecifics team, we are at the forefront of developing innovative cancer immunotherapies, including Volrustomig (PD1/CTLA-4) and Rilvegostomig (PD1/TIGIT). Our ambition is to become the #1 IO of choice across tumors and combinations — breaking the limits of IO sensitivity by maximizing CTLA-4 inhibition, unlocking new patient populations, and competing head-to-head against IO Standards of Care. The Director of IO Bioinformatics will be integral to this mission, leveraging computational biology, multi-omics, and AI to derive insights that inform clinical development, regulatory submissions, and strategic positioning for these bispecific assets.

Requirements

PhD (or equivalent) in Computational Biology, Bioinformatics, Data Science, or related field, with strong focus on Immuno-Oncology and substantial expertise in multi-omics and AI/ML.
Significant experience analyzing complex multi-omic biomarkers in Phase I to Phase III oncology clinical trials, including ctDNA, TMB, RNAseq, and proteomics, with proficiency in survival analysis.
Deep understanding of molecular mechanisms driving patient response or resistance to IO bispecifics, including T-cell activation, clonal expansion, and tumor microenvironment modulation.
Highly developed communication skills, with ability to translate complex multi-omics and AI-driven insights into actionable narratives for scientific, clinical, and regulatory stakeholders.
Proven ability to lead multiple simultaneous projects within a cross-functional environment spanning TMLs, EDS, CPM, Bioscience, and external partners.
Outstanding publication record in immuno-oncology, translational bioinformatics, and biomarker discovery.
Expertise in R and/or Python, with extensive Unix and HPC experience for large-scale multi-omics and AI applications.

Responsibilities

Serve as Strategic Bioinformatics Lead for IO Bispecifics: Guide bioinformatics efforts for Volrustomig and Rilvegostomig across tumor indications, supporting our goal to "BREAK limits of IO sensitivity" and unlock new patient populations.
Lead Analysis of Phase I-III Clinical Trials: Apply multi-omics and AI/ML to inform clinical development, regulatory submissions, and strategic decision-making. Drive generation of baseline and pharmacodynamic (PD) biomarkers, guiding tumor selection and companion diagnostic (CDx) development. Understand MoA and CoC for both bispecifics. Advance Computational Pathology (CPM) and Multiplex Immunofluorescence (mIF) analyses for response biomarker profiling across IO bispecific programs. Lead proteomics efforts for bulk protein quantification and investigation of PD-1 degradation, including Laser Microdissection (LMD) to analyze the tumor microenvironment. Optimize flow cytometry data analysis for peripheral immune profiling, including receptor occupancy and evaluation of PD-1 internalization from clinical PBMC samples. Collaborate end-to-end with ImmunAI for processing AZ clinical blood and tumor samples and benchmarking against PDx/SoC data.
Build IO Bispecifics Bioinformatics Strategy in Close Partnership: Collaborate with Translational Medicine Leads (TMLs), Clinical Development, EAI, and Computational Pathology. Integrate diverse data types — RNAseq, TCRseq, ctDNA, cytokines, spatial transcriptomics — into robust biological insights.
Apply Advanced Computational Approaches: Leverage AstraZeneca's rich clinical and pre-clinical data to understand MoA, resistance, patient selection, and clinical differentiation for our bispecific assets.
Influence Data Generation Strategy: Partner with TMLs to ensure generation of high-quality data assets and effective utilization of patient metadata within collaborative frameworks. Contribute to biomarker plans for early oncology studies.
Publish and Collaborate: Contribute to high-impact publications and present at major conferences (AACR, ASCO, ESMO). Engage in strategic industry and academic collaborations, including building the "AZ AMICA" single-cell platform.