Principal Bioinformatics Scientist at AccuraGen

Milpitas, California, United States -

Full Time

Start Date

Immediate

Expiry Date

01 Jun, 26

Salary

0.0

Posted On

03 Mar, 26

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Statistical Modeling, Algorithmic Methods, NGS, Variant Detection, MRD Calling, cfDNA Analysis, WGS Data, Python, C++, Rust, Java, Machine Learning, Assay Development, Validation, Design Controls, Root-Cause Analysis

Industry

Biotechnology

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 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 Health Care Plan (Medical, Dental & Vision) Retirement Plan (401k, IRA) Paid Time Off (Vacation, Sick & Public Holidays)

Responsibilities

This role involves leading the development and improvement of statistical and algorithmic methods for NGS-based variant detection and minimal residual disease calling, focusing on tumor/normal calling and ultra-low-frequency mutation detection in cfDNA. Key duties include providing technical leadership for somatic variant-calling methods, leading the development of MRD-calling algorithms, and architecting production-grade algorithms in partnership with engineering.