Cancer Bioinformatician​/Senior Analyst​/Senior Computational Scientist

We are recruiting a Bioinformatics Lead to build and continuously improve the computational analysis platform supporting high-sensitivity circulating tumor DNA (ctDNA) assay development and translational clinical research. This role will support NGS data processing, quality control frameworks, error suppression, variant detection, and reporting across tumor-informed and tumor-naïve workflows. The successful candidate will work closely with wet-lab scientists and clinicians to enable rapid iteration, reproducibility, and scalability, with an emphasis on ultra-low allele fraction detection and rigorous translational study support.

• Develop and maintain computational workflows supporting ctDNA-focused targeted sequencing analyses. Implement robust quality control metrics, acceptance criteria, and failure triage processes for high-depth sequencing runs. Generate analysis outputs and summaries to support translational studies, manuscripts, and grant applications. Contribute to continuous improvement of analytic performance (sensitivity/specificity) for ultra-low VAF detection and MRD-related applications.

• Partner with wet-lab and clinical teams to align assay design, sample processing, and analytic outputs; participate in troubleshooting and iterative optimization. Support study design discussions, analytic endpoint definitions, and interpretation of results for translational research programs.

• Support best practices for data governance, provenance, documentation, and reproducibility in handling human genomic data. Work with institutional resources to implement secure computational environments and appropriate data access practices.

• Mentor junior analysts as the program grows; contribute to hiring, onboarding, and training as needed. Help establish standards for analytic workflows, documentation, and communication across the research team.

• PhD in Bioinformatics, Computational Biology, Genetics/Genomics, Computer Science, Biostatistics, or related field;
  or MS with substantial relevant experience (track/title commensurate with credentials).
  Demonstrated experience analyzing ctDNA NGS data
  , including ultra-low allele fraction detection and/or MRD-related workflows. Strong NGS fundamentals: alignment, variant calling, QC, annotation, and interpretation-ready output generation. Proficiency in Python and/or R; strong comfort with Linux/Unix environments. Experience implementing reproducible analytic workflows and maintaining code in collaborative environments (e.g., version control). Track record of delivering robust pipelines used repeatedly for real datasets. Strong communication skills and ability to operate effectively in a multidisciplinary translational environment.

Any of the following (or similar) would be a plus:

• Method development experience related to error suppression, background error modeling, consensus approaches, or sensitivity/specificity benchmarking for ultra-low VAF detection. Experience designing computational validation plans (e.g., precision/recall, LOD, reproducibility) and supporting assay/pipeline iteration. Experience with FFPE tumor tissue sequencing analysis and variant calling (or similar challenging specimen types with artifact-aware calling and QC). Familiarity with HIPAA-aligned compute environments and practices for handling human genomic data;
  
  experience with secure cloud environments (AWS/GCP/Azure). Experience working in or alongside clinical genomics settings and documentation practices supportive of eventual clinical validation. Experience mentoring analysts/engineers and/or leading pipeline development across multiple projects.

Physical Requirements:

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Note:

The following descriptions are applicable to this section: Continuous - 6-8 hours per shift; Frequent - 2-6 hours per shift; Infrequent - 0-2 hours per shift) Ability to perform job functions in an upright position. (Frequent) Ability to perform job functions in a seated position. (Frequent) Ability to perform…