Senior Scientist, Bioinformatics​/Computational Biology

Overview

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.

• 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.

• 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
  , feature Counts
  , 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/Git Hub and reproducible research practices, including Nextflow or similar workflow…