Senior Staff Bioinformatics Scientist

AccuraGen•San Jose, CA

20h

About The Position

As a Senior Staff Bioinformatics Scientist at AccuScan Science, 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.

Requirements

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

Nice To Haves

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

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