Senior Scientist, Bioinformatics / Computational Biology

AstraZeneca•Cambridge, MA

23h•$115,992 - $172,672•Onsite

About The Position

This role focuses on leveraging multi-omics, comparative genomics, and agentic AI to accelerate the discovery and clinical development of vaccines and immune therapies. The Senior Scientist will transform complex human and pathogen datasets into actionable insights to guide antigen design, patient stratification, and translational strategy. The position is based in Cambridge, MA, and involves working in a collaborative, multidisciplinary environment with immunologists, molecular biologists, and data scientists. The role offers the opportunity to influence study design, guide go/no-go decisions, and advance novel immune-based therapies toward patients by working at the intersection of computation and experimentation, designing reproducible pipelines on HPC and cloud platforms, and partnering closely with laboratory teams for rapid iteration.

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 track record of independent research through publications, conference presentations, or successful project delivery.
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, including alignment tools (STAR, BWA, Bowtie2), quantification tools (Salmon, featureCounts, HTSeq), and variant calling tools (GATK, bcftools).
Familiarity with RNA-seq analysis workflows, including differential expression methods (DESeq2, edgeR, limma), pathway analysis, and gene set enrichment approaches (ssGSEA, MSigDB).
Experience working in Linux/Unix environments and with HPC job schedulers (SLURM, SGE, PBS) and/or cloud computing platforms (AWS, GCP).
Working knowledge of Git/GitHub and reproducible research practices, including Nextflow or similar workflow managers.
Solid understanding of molecular biology fundamentals, genome annotation, and public bioinformatics databases (NCBI, Ensembl, UniProt, PDB).
Foundational knowledge of machine learning concepts and applied statistics relevant to biomarker discovery and genomic data.
Strong analytical thinking, creative problem-solving, and the ability to translate complex datasets into actionable biological insights.
Excellent written and verbal communication skills, a collaborative mindset, intellectual curiosity, and the ability to manage multiple priorities and deliver results within timelines.

Nice To Haves

Experience in at least one therapeutic area—infectious diseases, oncology, or inflammatory disease.
Experience with comparative genomics and microbial or viral genome analysis, including pangenome methods, AMR gene detection, and phylogenetics.
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.
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 (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.
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 robust analyses across genomics, bulk and single-cell transcriptomics, and multi-omics to answer program-critical questions with statistical rigor.
Assemble genomes, call variants, and perform comparative genomics and phylogenetic analyses on 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, translating model outputs into actionable 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 to accelerate discovery and improve developer productivity.
Propose computationally informed experiments, interpret results, and refine study designs to improve confidence and reduce cycle time in collaboration with experimental scientists.
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, 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.
Stay current with emerging tools in bioinformatics, AI/ML, and agentic AI, piloting new approaches, sharing learnings, and scaling successful methods across the portfolio.