Senior Bioinformatics Scientist

As a Senior Staff Bioinformatics Scientist, you will lead the development and improvement of statistical and algorithmic methods for NGS-based variant detection and minimal residual disease (MRD) calling. This role focuses on tumor/normal variant calling in tissue samples as well as ultra–low-frequency mutation detection in cfDNA.

You will work closely with assay development, bioinformatics engineering, and R&D teams to translate new technologies into robust, production-ready analytical pipelines. The ideal candidate brings deep statistical modeling expertise, strong hands-on implementation skills, and experience working with WGS or large-scale sequencing data. Prior exposure to regulated (FDA/IVD) environments and machine learning is a strong plus.

Key Responsibilities

• Improve and extend somatic variant-calling algorithms for tumor tissue and cfDNA-based mutation detection
• Develop, validate, and refine MRD-calling algorithms with an emphasis on sensitivity, specificity, and robustness
• Design and implement benchmarking, evaluation, and quality control (QC) methodologies
• Lead troubleshooting efforts, including root-cause analysis of analytical or pipeline failures, and drive corrective actions
• Implement algorithms in production-quality code and collaborate with engineering teams to integrate methods into scalable pipelines and workflows
• Partner with assay development teams on new technologies and assay iterations requiring customized analysis strategies and algorithm development
• Document analytical methods, validation results, and design decisions; clearly communicate findings, limitations, and trade-offs to technical and cross-functional stakeholders

Requirements / Qualifications

• Ph.D. in Statistics, Biostatistics, Computer Science, Bioinformatics, Computational Biology, Applied Mathematics, or a related field, with relevant postdoctoral or industry experience
• Strong foundation in statistical inference and modeling, including uncertainty quantification and decision thresholding
• Prior experience working with genomics data, including WGS or large-scale NGS datasets, and a solid understanding of technical and biological noise sources
• Demonstrated software implementation skills in Python and/or a performance-oriented language (e.g., C++, Rust, Java), with experience writing maintainable, testable, production-quality code
• Familiarity with standard genomics data formats and tooling (e.g., FASTQ, BAM/CRAM, VCF) and common processing workflows
• Experience working in regulated product development environments (e.g., FDA, IVD), including documentation practices, analytical validation, and design controls
• Excellent communication and collaboration skills, with the ability to work effectively across research, engineering, and assay development teams

Preferred Qualifications

• Hands-on experience with cfDNA analysis and/or MRD detection, including ultra–low-frequency variant calling and/or epigenetics-based analyses
• Machine learning experience, particularly in settings involving class imbalance, model evaluation, calibration, and decision optimization
• Experience collaborating closely with assay development teams on experimental design, data analysis planning, and iterative assay optimization