Job details
About this role
Our bioinformatics team develops and supports next-generation sequencing assays for oncology and other human genomic applications. We are seeking a hands-on bioinformatics scientist who can combine strong programming skills with human genomics expertise to support the design, evaluation, and improvement of targeted NGS assays.
This role is well suited to someone who enjoys working directly with sequencing data, investigating unexpected assay behavior, developing reproducible analyses, and translating complex findings into clear technical conclusions. The scientist will work closely with molecular biology, software, product development, regulatory, and clinical teams.
Responsibilities:
- Develop reproducible analyses and computational workflows in Python, R, and/or Bash to evaluate NGS assay performance.
- Analyze DNA and RNA sequencing data generated from targeted assays, including SNVs, indels, CNVs, fusions, coverage, assay artifacts, and other performance characteristics.
- Investigate assay failures and unexpected results using sequencing data, genomic context, experimental metadata, and appropriate statistical or computational approaches.
- Support development and optimization of targeted amplicon-based NGS assay workflows.
- Use public genomic and oncology resources such as ClinVar, gnomAD, TCGA/cBioPortal, dbSNP, and primary literature to research genomic targets and interpret assay results.
- Work closely with molecular biology scientists to translate experimental observations into testable computational analyses and assay improvements.
- Develop tools, scripts, and analytical methods that improve the efficiency, robustness, or interpretability of assay development.
- Maintain well-organized, reproducible analyses with clear version control, data provenance, and documentation.
- Communicate findings through concise technical reports, figures, presentations, protocols, and other documentation that can be understood by both computational and non-computational collaborators.
- Manage multiple development projects and analyses while maintaining clear records of assumptions, methods, results, and outstanding questions.
Qualifications:
- MS or PhD in bioinformatics, computational biology, genetics, genomics, or a related field.
- Demonstrated hands-on programming experience in Python, R, or another general-purpose scientific programming language. Candidates should be comfortable writing their own analysis code rather than relying primarily on graphical or preconfigured analysis tools.
- Strong knowledge of human genomics and genetic variation.
- Experience analyzing human NGS data, including alignment, QC, variant analysis, and interpretation.
- Familiarity with common genomics file formats and tools such as FASTQ, BAM/CRAM, VCF, BED, BWA, samtools, bedtools, GATK, or equivalent tools.
- Demonstrated ability to independently investigate complex datasets, identify the source of analytical or experimental problems, and communicate conclusions.
- Strong scientific writing and documentation skills, with evidence of producing reproducible analyses, technical documentation, protocols, reports, publications, or similar work.
- Strong organizational skills and ability to manage multiple analyses and development activities with appropriate documentation and follow-through.
- Ability to communicate effectively with molecular biologists, software engineers, and other cross-functional collaborators.
Preferred Qualifications:
- Experience developing or analyzing targeted NGS assays using amplicon sequencing or hybrid capture.
- Experience with oncology genomics, somatic variant analysis, low-input DNA, FFPE, cfDNA, or other clinically relevant sample types.
- Experience evaluating assay performance metrics such as coverage, uniformity, sensitivity, specificity, background error, limit of detection, or variant-calling performance.
- Experience with primer/probe design, target-region design, or computational support of molecular assay development.
- Experience developing new algorithms or analytical methods for genomic data.
- Experience with reproducible development practices using Git, Jira, containers, or similar tools.