Senior Cancer Bioinformatician & ctDNA Analytics Lead

MUSC-Radiation Medicine; Charleston (SC); On-site; USD 120,000 - 170,000; Full time

Job summary

Medical University of South Carolina seeks a Bioinformatics Lead to build and enhance the ctDNA analysis platform supporting high-sensitivity assay development and translational clinical research. The role involves NGS data processing, QC frameworks, error suppression, variant detection, and reporting across workflows.

The successful candidate will collaborate with wet-lab scientists and clinicians to enable rapid iteration, reproducibility, and scalability, emphasizing ultra-low allele fraction

Qualifications

• PhD or MS with substantial relevant experience, commensurate with credentials.
• Demonstrated ctDNA NGS data analysis experience including ultra-low allele fraction detection/MRD workflows.
• Strong NGS fundamentals: alignment, variant calling, QC, annotation, and interpretation-ready outputs.
• Proficiency in Python and/or R; comfortable with Linux/Unix; experience with reproducible workflows and version control.
• Strong communication and ability to operate in a multidisciplinary translational environment.

Responsibilities

• Develop and maintain computational workflows for ctDNA-focused sequencing analyses with QC and failure triage.
• Collaborate with wet-lab and clinical teams on study design and interpretation of analytic endpoints.
• Support data governance, provenance, and reproducibility with secure compute environments.
• Mentor analysts and contribute to hiring, onboarding, and training; standardize analytic workflows.

Skills

ctDNA NGS analysis

Python

R

Linux/Unix

Pipeline development

Effective communication

Education

PhD in Bioinformatics or related field

MS with substantial relevant experience

Tools

Git

AWS/GCP/Azure

Nextflow

Job description

Medical University of South Carolina seeks a Bioinformatics Lead to build and enhance the ctDNA analysis platform supporting high-sensitivity assay development and translational clinical research. The role involves NGS data processing, QC frameworks, error suppression, variant detection, and reporting across workflows.

The successful candidate will collaborate with wet-lab scientists and clinicians to enable rapid iteration, reproducibility, and scalability, emphasizing ultra-low allele fraction