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)

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