Principal Bioinformatics Scientist

Principal Bioinformatics Scientist Milpitas, CA Job Details Full-time 1 day ago Benefits Health insurance Dental insurance 401(k) Paid time off Vision insurance Qualifications Bioinformatics Doctoral degree in statistics Next generation sequencing Rust (programming language) Statistics Computer Science Applied Mathematics Biostatistics-based research Tooling Technical documentation Software coding Computational Biology Scalable systems Assay development Java 8 years Doctoral degree in Computer Science Statistical analysis Analysis skills C++ Ph.D. in statistics Doctor of Philosophy Statistical modeling Experimental design Scalability Design controls Technical troubleshooting support Root cause analysis Implementing machine learning algorithms Benchmarking Senior level Cross-functional collaboration Research & development Bioinformatics data analysis Communication skills Project stakeholder communication Python Cross-functional communication FDA regulations Full Job Description As a Principal Bioinformatics Scientist at AccuScan Sciences, 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 Provide scientific and technical leadership for the design, evolution, and long‑term roadmap of somatic variant‑calling methods for tumor tissue and cfDNA applications Lead the development, validation, and optimization of MRD‑calling algorithms, setting standards for sensitivity, specificity, robustness, and clinical relevance Define and own benchmarking frameworks, performance metrics, and QC strategies used to evaluate analytical methods across platforms, assays, and data types Serve as a senior technical authority for troubleshooting complex analytical and pipeline issues, performing root‑cause analysis, and driving durable, system‑level solutions Architect and implement production‑grade algorithms, partnering with bioinformatics engineering to ensure scalability, reliability, and maintainability of analytical pipelines Act as a key scientific partner to assay development teams, shaping experimental design, data analysis strategies, and algorithmic adaptations for new and evolving technologies Establish best practices for analytical documentation, validation reporting, and design controls; communicate technical trade‑offs, limitations, and recommendations to senior technical, clinical, and cross‑functional stakeholders Requirements Ph.D. in Statistics, Biostatistics, Computer Science, Bioinformatics, Computational Biology, Applied Mathematics, or a related field, with 8+ years of domain 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 Familiarity with standard genomics data formats and tooling (e.g., FASTQ, BAM/CRAM, VCF) and common processing workflows 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 Excellent communication and collaboration skills, with the ability to work effectively across research, engineering, and assay development teams 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 Experience working in regulated product development environments (e.g., FDA, IVD), including documentation practices, analytical validation, and design controls Benefits Health Care Plan (Medical, Dental & Vision) Retirement Plan (401k, IRA) Paid Time Off (Vacation, Sick & Public Holidays)