Are you ready to harness multi-omics, comparative genomics, and agentic AI to accelerate vaccines and immune therapies from discovery to the clinic? In this role, you will transform complex human and pathogen datasets into clear, decision-driving insights that shape antigen design, patient stratification, and translational strategy across high-priority programs.

Based in Cambridge, MA you will work in a collaborative, multidisciplinary environment alongside immunologists, molecular biologists, and data scientists. If you thrive at the intersection of computation and experiment—designing reproducible pipelines on HPC and cloud platforms while partnering closely with the lab to iterate rapidly—this role offers the opportunity to influence study design, guide go/no-go decisions, and help advance novel immune-based therapies toward patients.

Accountabilities

• You will design, implement, and deliver robust analyses across genomics, bulk and single-cell transcriptomics, and multi-omics to answer program-critical questions with statistical rigor. You will assemble genomes, call variants, and perform comparative genomics and phylogenetic analyses on bacterial and viral pathogens to inform antigen selection and surveillance strategy.
• You will apply machine learning and statistical modeling to discover biomarkers, stratify patients, predict antigen immunogenicity, and forecast treatment response, translating model outputs into actionable program recommendations. You will also build, optimize, and maintain reproducible workflows using HPC schedulers and AWS to scale analyses, reduce turnaround time, and ensure traceability.

In addition, you will design and integrate LLM-powered agentic workflows for literature mining, data extraction, and pipeline orchestration to accelerate discovery and improve developer productivity. Working closely with experimental scientists, you will propose computationally informed experiments, interpret results, and refine study designs to improve confidence and reduce cycle time.

You will generate translational insights through differential expression, pathway enrichment, and functional annotation, connecting molecular signals to biological mechanisms and clinical hypotheses. You will produce publication-quality visualizations and reports, present findings clearly to cross-functional stakeholders, and champion version control, workflow managers, and reproducible research practices to strengthen code quality and method sharing across programs.

Finally, you will stay current with emerging tools in bioinformatics, AI/ML, and agentic AI, piloting new approaches, sharing learnings, and scaling successful methods across the portfolio.

Essential Skills and Experience

• You should have a PhD in Bioinformatics, Computational Biology, Genomics, Molecular Biology, Computer Science, or a closely related quantitative discipline, with 2–5 years of industry experience.
• Alternatively, you may have an MS in a relevant discipline with 4–6 years of industry experience in bioinformatics, computational biology, or genomics.
• A demonstrated track record of independent research through publications, conference presentations, or successful project delivery is expected.
• You should bring proficiency in R and/or Python for genomic data analysis, statistical computing, and data visualization, including tools such as ggplot2, Bioconductor, tidyverse, pandas, and scikit-learn.
• Hands-on experience with NGS data analysis is required, including alignment tools such as STAR, BWA, and Bowtie2; quantification tools such as Salmon, featureCounts, and HTSeq; and variant calling tools such as GATK and bcftools.
• You should be familiar with RNA-seq analysis workflows, including differential expression methods such as DESeq2, edgeR, and limma, as well as pathway analysis and gene set enrichment approaches such as ssGSEA and MSigDB. Experience working in Linux/Unix environments and with HPC job schedulers such as SLURM, SGE, or PBS, and/or cloud computing platforms such as AWS or GCP, is important.

You should also have working knowledge of Git/GitHub and reproducible research practices, including Nextflow or similar workflow managers. A solid understanding of molecular biology fundamentals, genome annotation, and public bioinformatics databases such as NCBI, Ensembl, UniProt, and PDB is required, along with foundational knowledge of machine learning concepts and applied statistics relevant to biomarker discovery and genomic data.

Success in this role will also require strong analytical thinking, creative problem-solving, and the ability to translate complex datasets into actionable biological insights. You should have excellent written and verbal communication skills, a collaborative mindset, intellectual curiosity, and the ability to manage multiple priorities and deliver results within timelines.

Desirable Skills and Experience

Experience in at least one therapeutic area—infectious diseases, oncology, or inflammatory disease—would be valuable.

We also welcome experience with comparative genomics and microbial or viral genome analysis, including pangenome methods, AMR gene detection, and phylogenetics.

Additional desirable experience includes building predictive and prognostic models using supervised and unsupervised machine learning methods on clinical or preclinical omics data; familiarity with deep learning frameworks such as PyTorch and TensorFlow; and exposure to biological foundation models such as ESM, EvolutionaryScale, scGPT, TranscriptFormer, and Evo.

We also value experience with or strong interest in agentic AI workflows for bioinformatics, including LLM-orchestrated pipelines, retrieval-augmented generation (RAG) for scientific literature, and tool-using AI agents that interact with databases and analysis tools. Proficiency with AI-assisted coding tools such as Claude Code or GitHub Copilot is a plus.

Exposure to single-cell RNA-seq tools such as Seurat, Scanpy, and CellRanger; knowledge of structural biology tools, protein modeling, or antigen/antibody design; and experience with containerization and infrastructure-as-code would also be beneficial. Familiarity with LLM APIs and prompt engineering for scientific applications, including structured output generation and multi-agent system design, is also desirable.

Why AstraZeneca

At AstraZeneca, ambitious science meets everyday collaboration. Here, bioinformaticians, immunologists, clinicians, and engineers come together to share knowledge openly, challenge ideas constructively, and learn from setbacks as they work toward better solutions. You will contribute across diverse therapy areas, with visibility into decisions that matter and support from leaders who encourage experimentation and innovation.

We pair rigorous scientific standards with creativity and value kindness alongside ambition. Most importantly, we connect each individual’s contribution to a clear purpose: translating insights into medicines that can change patients’ lives.

If you are ready to turn data, models, and modern AI into faster, smarter decisions for patients, we encourage you to apply and show us how you can make an impact from day one.

The annual base pay for this position ranges from $115,992.00 - $172,671.60. Our positions offer eligibility for various incentives—an opportunity to receive short-term incentive bonuses, equity-based awards for salaried roles and commissions for sales roles. Benefits offered include qualified retirement programs, paid time off (i.e., vacation, holiday, and leaves), as well as health, dental, and vision coverage in accordance with the terms of the applicable plans.

Date Posted

Closing Date

Our mission is to build an inclusive environment where equal employment opportunities are available to all applicants and employees. In furtherance of that mission, we welcome and consider applications from all qualified candidates, regardless of their protected characteristics. If you have a disability or special need that requires accommodation, please complete the corresponding section in the application form.