Senior Scientist, Bioinformatics / Computational Biology

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 **differe…