Computational Biologist I

Foundation Medicine

1d•Hybrid

About The Position

The Computational Biologist I contributes to the early innovation of novel bioinformatics methods and genomic signatures in oncology. Leveraging Foundation Medicine’s FoundationCORE dataset of 800,000+ samples and the Flatiron Health-Foundation Medicine joint Clinico-Genomic Database of 100,000+ samples with annotated clinical outcomes, this role explores novel patterns in cancer biology. The position plays a key role in conducting original research, generating insights, and preparing high-impact scientific abstracts, presentations, and peer-reviewed manuscripts. The work drives foundational understanding that supports clinical diagnostics and translational research in oncology. In addition to research, this role develops sub-specialization in the data architecture supporting the team’s efforts, including pipeline workflow development, cloud-based data storage, and maintaining data quality and integrity.

Requirements

Bachelor’s degree in related field and 6+ years of research experience preferred in the life sciences industry; OR, Master’s and 2+ year of research experience preferred in the life sciences industry; OR, PhD and no experience
Proficiency in Unix or Unix-like operating systems
Proficiency in one or more programming languages such as Python, R, SQL, or similar scientific computing languages; familiarity with other programming or scripting languages
Fundamental understanding of molecular biology and NGS principles (e.g., DNA-seq, library prep, target enrichment)
Effective collaboration and communication skills, including scientific writing and presenting data to multidisciplinary teams
Understanding of HIPAA and the importance of patient data privacy
Commitment to reflect FMI's values of Integrity, Courage, and Passion

Nice To Haves

1+ years of industry or post-doctoral research experience in cancer biology, bioinformatics, or a related field using NGS data
Familiarity with cloud computing platforms (e.g. AWS)
Experience using version control tools (e.g. Git)
Experience working with public datasets (e.g., TCGA, cBioPortal, Genomic Data Commons)
Competence in basic statistical methods and applications in bioinformatics

Responsibilities

Develop and evaluate potential signatures and biomarkers using large-scale datasets; investigate genomic mechanisms underlying oncogenesis.
Apply statistical analyses, analyze genomic data via HPC clusters, and improve assay performance.
Organize, visualize, and interpret data in a high-throughput, data-intensive environment.
Collaborate with internal cross-functional teams — including R&D, Medical, Commercial, Biopharma, and Regulatory — and engage with external collaborators to support enterprise goals.
Work closely with molecular biologists, computational scientists, and regulatory teams to inform the feasibility and planning of translational next-generation sequencing (NGS) assays.
Maintain awareness of current bioinformatics methods and diagnostic oncology literature.
Prepare and present results in internal meetings and contribute to scientific literature.
Design and refine bioinformatics algorithms, workflows, and analysis pipelines; manage cloud-based data infrastructure and ensure data quality.
Build, maintain, and enhance internal data workflows and computational pipelines; implement and uphold best practices in data architecture and cloud-based data systems
Other duties as assigned.