Senior Scientist, Bioinformatics / Computational Biology

Senior scientist role at AstraZeneca's Cambridge, MA site focused on applying bioinformatics, multi-omics, and computational methods to accelerate vaccine and immune therapy development. The position involves designing genomic analyses, building machine learning models for biomarker discovery, and collaborating with experimental teams to drive translational insights.

Responsibilities

• Design and execute genomic, transcriptomic, and multi-omics analyses to address program-critical research questions with statistical rigor
• Perform genome assembly, variant calling, comparative genomics, and phylogenetic analyses on pathogens to inform antigen selection and surveillance
• Apply machine learning and statistical modeling to discover biomarkers, stratify patients, and predict immunogenicity and treatment response
• Build and maintain reproducible, scalable workflows using HPC and AWS; design and integrate LLM-powered agentic tools for literature mining and pipeline orchestration
• Collaborate with experimental scientists to propose computationally informed studies, interpret results, and communicate findings through publication-quality visualizations and reports

Requirements

• PhD in Bioinformatics, Computational Biology, Genomics, or related quantitative field with 2–5 years industry experience, OR MS with 4–6 years industry experience
• Proficiency in R and/or Python for genomic analysis, statistical computing, and visualization; familiarity with tools like Bioconductor, pandas, scikit-learn
• Hands-on NGS data analysis experience including alignment, quantification, and variant calling tools
• Strong experience with RNA-seq workflows, differential expression analysis, pathway enrichment, and gene set methods
• Linux/Unix environment competency; experience with HPC job schedulers (SLURM, SGE, PBS) and/or cloud platforms (AWS, GCP)
• Working knowledge of Git/GitHub, workflow managers like Nextflow, and reproducible research practices
• Solid understanding of molecular biology, genome annotation, and major bioinformatics databases; foundational machine learning and statistics knowledge