Senior Scientist, Bioinformatics / Computational Biology

AstraZeneca•Cambridge, MA

1d•$115,992 - $172,672•Onsite

About The Position

Based in Cambridge, Massachusetts, the Senior Scientist will work in a collaborative, multidisciplinary environment with immunologists, molecular biologists, and data scientists. The role applies multi-omics, comparative genomics, and agentic AI to accelerate vaccine and immune-therapy programs, converting complex human and pathogen datasets into insights that inform antigen design, patient stratification, and translational strategy. The scientist will design and deliver rigorous analyses across genomics, bulk and single-cell transcriptomics, and multi-omics; analyze bacterial and viral pathogens; develop machine-learning models; and build reproducible workflows on HPC and cloud platforms. The role partners closely with experimental scientists to shape studies, interpret findings, and guide program decisions. It also develops LLM-powered workflows for literature mining, data extraction, and pipeline orchestration, and communicates results through reports, visualizations, and cross-functional presentations. A PhD with 2–5 years of industry experience, or an MS with 4–6 years of relevant industry experience, is expected. The position calls for strong programming, NGS, RNA-seq, statistical, and reproducible-research skills, along with the ability to turn complex data into actionable biological insights. The annual base salary range is $115,992.00–$172,671.60. The position is eligible for applicable incentives and benefits, including retirement programs, paid time off, and health, dental, and vision coverage.

Requirements

PhD in Bioinformatics, Computational Biology, Genomics, Molecular Biology, Computer Science, or a closely related quantitative discipline, with 2–5 years of industry experience; alternatively, an MS in a relevant discipline with 4–6 years of industry experience in bioinformatics, computational biology, or genomics.
Demonstrated independent research track record through publications, conference presentations, or successful project delivery.
Proficiency in R and/or Python for genomic data analysis, statistical computing, and visualization; examples include ggplot2, Bioconductor, tidyverse, pandas, and scikit-learn.
Hands-on NGS data analysis experience, 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.
Familiarity with RNA-seq workflows, differential expression methods such as DESeq2, edgeR, and limma, and pathway analysis and gene-set enrichment approaches such as ssGSEA and MSigDB.
Experience with Linux/Unix and HPC job schedulers such as SLURM, SGE, or PBS, and/or cloud platforms such as AWS or GCP.
Working knowledge of Git/GitHub and reproducible research practices, including Nextflow or similar workflow managers.
Understanding of molecular biology fundamentals, genome annotation, and public bioinformatics databases such as NCBI, Ensembl, UniProt, and PDB.
Foundational knowledge of machine-learning concepts and applied statistics relevant to biomarker discovery and genomic data.
Strong analytical thinking, creative problem-solving, and ability to translate complex datasets into actionable biological insights.
Excellent written and verbal communication, a collaborative mindset, intellectual curiosity, and ability to manage multiple priorities and deliver within timelines.

Nice To Haves

Experience in infectious diseases, oncology, or inflammatory disease.
Experience with comparative genomics and microbial or viral genome analysis, including pangenome methods, AMR gene detection, and phylogenetics.
Experience building predictive and prognostic models using supervised and unsupervised machine learning on clinical or preclinical omics data.
Familiarity with deep-learning frameworks such as PyTorch and TensorFlow.
Exposure to biological foundation models such as ESM, EvolutionaryScale, scGPT, TranscriptFormer, and Evo.
Experience with or strong interest in agentic AI workflows for bioinformatics, including LLM-orchestrated pipelines, retrieval-augmented generation 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.
Exposure to single-cell RNA-seq tools such as Seurat, Scanpy, and CellRanger.
Knowledge of structural biology tools, protein modeling, or antigen/antibody design.
Experience with containerization and infrastructure-as-code.
Familiarity with LLM APIs and prompt engineering for scientific applications, including structured output generation and multi-agent system design.

Responsibilities

Design, implement, and deliver statistically rigorous analyses of genomics, bulk and single-cell transcriptomics, and multi-omics data to address program-critical questions.
Assemble genomes, call variants, and perform comparative genomics and phylogenetic analyses of bacterial and viral pathogens to inform antigen selection and surveillance strategy.
Apply machine learning and statistical modeling to discover biomarkers, stratify patients, predict antigen immunogenicity, and forecast treatment response; translate results into program recommendations.
Build, optimize, and maintain reproducible workflows using HPC schedulers and AWS to scale analyses, reduce turnaround time, and ensure traceability.
Design and integrate LLM-powered agentic workflows for literature mining, data extraction, and pipeline orchestration.
Partner with experimental scientists to propose computationally informed experiments, interpret results, and refine study designs.
Generate translational insights through differential expression, pathway enrichment, and functional annotation, connecting molecular signals to biological mechanisms and clinical hypotheses.
Produce publication-quality visualizations and reports and present findings to cross-functional stakeholders.
Promote version control, workflow managers, and reproducible research practices to improve code quality and method sharing.
Stay current with bioinformatics, AI/ML, and agentic AI tools; pilot new approaches, share learnings, and scale successful methods across programs.