What are the responsibilities and job description for the Senior Computational Scientist I position at Broad Institute of MIT and Harvard?
The Translational Analysis Group (TAG) at [1] the Broad Institute of MIT and Harvard, led by Dr. Mark Fleharty, is seeking a Senior Computational Scientist I with extensive experience building and maintaining NGS pipelines. In this full-time, on-site role, you will design and implement next-generation workflows for liquid biopsy assays in pediatric oncology. You’ll collaborate with biologists, clinicians, data scientists, and software engineers to translate novel scientific insights into production-ready computational tools. TAG’s mission is to move liquid biopsy from proof-of-concept to clinically validated assays for sensitive cancer detection and monitoring in children.
Responsibilities
As a Senior Computational Scientist I, you will:
* Lead the design, implementation, and continuous improvement of robust, reproducible pipelines for analysis of liquid biopsy NGS data—encompassing sample QC, alignment, error correction, variant calling, annotation, and results delivery.
* Develop and validate novel algorithms for ultra-sensitive detection of somatic variants, rare event calling, and multi-modal biomarker integration.
* Drive performance benchmarking and analytical validation using both synthetic and clinical datasets; partner with wet lab and clinical teams to refine pipeline inputs and outputs.
* Collaborate closely with software engineers to ensure scalable, production-ready workflows and with wet lab scientists to ensure computational methods fit experimental realities.
* Champion best practices in version control, code review, automated testing, and documentation (e.g., GitHub, containerization, CI/CD pipelines)
* Mentor and advise junior computational team members; foster a culture of knowledge sharing, curiosity, and continual learning.
* Present findings and methods to internal and external stakeholders, contribute to publications, grants, and regulatory submissions.
* Remain at the forefront of new technologies and methods in the liquid biopsy and bioinformatics space, actively shaping our analytical strategy and scientific vision.
Qualifications:
* PhD in Bioinformatics, Computational Biology, Computer Science, Engineering, or related quantitative discipline.
* Minimum of 3 years of related experience.
* Proven expertise developing, optimizing, and maintaining advanced bioinformatics pipelines for NGS data, preferably in liquid biopsy, minimal residual disease, or somatic variant detection contexts.
* Mastery of at least two programming languages, including one higher-level language such as Python or R, and one lower-level language such as C or Rust, for data analysis and pipeline development; experience with workflow orchestration tools (e.g., Nextflow, Cromwell) and cloud or distributed computing environments.
* Deep understanding of molecular biology underlying liquid biopsy technologies—cfDNA/ctDNA biology, error suppression, and the statistical and technical nuances of ultra-low-frequency variant detection.
* Demonstrated experience benchmarking analytical methods, using gold-standard truth sets and orthogonal validation, and familiarity with clinical assay validation requirements (e.g., CLIA, CAP, frameworks)
* Excellence in scientific communication—clear, actionable reporting, documentation, and team-wide dissemination of best practices.
* Strong track record of cross-functional teamwork, mentoring, and capacity-building in computational teams.
* Self-motivated, adaptable, detail-oriented; able to manage multiple priorities in a fast-evolving environment.
* Commitment to scientific rigor, reproducibility, and open, collaborative problem-solving.
References
Visible links
1. https://www.broadinstitute.org/
Responsibilities
As a Senior Computational Scientist I, you will:
* Lead the design, implementation, and continuous improvement of robust, reproducible pipelines for analysis of liquid biopsy NGS data—encompassing sample QC, alignment, error correction, variant calling, annotation, and results delivery.
* Develop and validate novel algorithms for ultra-sensitive detection of somatic variants, rare event calling, and multi-modal biomarker integration.
* Drive performance benchmarking and analytical validation using both synthetic and clinical datasets; partner with wet lab and clinical teams to refine pipeline inputs and outputs.
* Collaborate closely with software engineers to ensure scalable, production-ready workflows and with wet lab scientists to ensure computational methods fit experimental realities.
* Champion best practices in version control, code review, automated testing, and documentation (e.g., GitHub, containerization, CI/CD pipelines)
* Mentor and advise junior computational team members; foster a culture of knowledge sharing, curiosity, and continual learning.
* Present findings and methods to internal and external stakeholders, contribute to publications, grants, and regulatory submissions.
* Remain at the forefront of new technologies and methods in the liquid biopsy and bioinformatics space, actively shaping our analytical strategy and scientific vision.
Qualifications:
* PhD in Bioinformatics, Computational Biology, Computer Science, Engineering, or related quantitative discipline.
* Minimum of 3 years of related experience.
* Proven expertise developing, optimizing, and maintaining advanced bioinformatics pipelines for NGS data, preferably in liquid biopsy, minimal residual disease, or somatic variant detection contexts.
* Mastery of at least two programming languages, including one higher-level language such as Python or R, and one lower-level language such as C or Rust, for data analysis and pipeline development; experience with workflow orchestration tools (e.g., Nextflow, Cromwell) and cloud or distributed computing environments.
* Deep understanding of molecular biology underlying liquid biopsy technologies—cfDNA/ctDNA biology, error suppression, and the statistical and technical nuances of ultra-low-frequency variant detection.
* Demonstrated experience benchmarking analytical methods, using gold-standard truth sets and orthogonal validation, and familiarity with clinical assay validation requirements (e.g., CLIA, CAP, frameworks)
* Excellence in scientific communication—clear, actionable reporting, documentation, and team-wide dissemination of best practices.
* Strong track record of cross-functional teamwork, mentoring, and capacity-building in computational teams.
* Self-motivated, adaptable, detail-oriented; able to manage multiple priorities in a fast-evolving environment.
* Commitment to scientific rigor, reproducibility, and open, collaborative problem-solving.
References
Visible links
1. https://www.broadinstitute.org/
