Associate Scientist / Scientist I, Computational Test Development

What You'll Do Assay Characterization & Validation

Execute data analyses supporting assay characterization across multiple epigenomic and NGS-based diagnostic tests — from raw data processing through performance metric generation

Design and implement quality assessment frameworks to evaluate assay and pipeline performance, including QC metric definition, threshold setting, and failure mode identification

Run and interpret validation experiments in close coordination with wet lab and senior computational team members, contributing to study execution against pre-defined protocols

Pursue defined research questions semi-independently — taking a scoped problem, designing the analytical approach, executing, and returning well-documented results

In Silico Simulation & Thresholding

Design and run in silico simulations to model assay behavior under variable conditions — including signal dropout, coverage non-uniformity, and input DNA variability

Develop and apply statistical approaches to threshold setting and performance boundary definition, supporting limit of detection and analytical range characterization

Systematically explore parameter sensitivity across bioinformatics pipelines to assess model robustness and inform feature selection

Regulatory Support & Communication

Produce clear, thorough documentation of all analyses code, methods, results, and interpretation to the standard required under design controls

Contribute to the authoring of SOPs and analytical summary reports

Partner closely with wet lab scientists to ensure computational analyses are grounded in experimental reality and that results are communicated in accessible, actionable terms

Present analytical findings clearly in team meetings and cross-functional settings

Contribute to a collaborative team environment by sharing code and knowledge openly and proactively

Who You Are MSc in computational biology, bioinformatics, biostatistics, or a closely related quantitative field with 3–4 years of industry or post-graduate research experience in a data-intensive biological or biomedical setting, Ph.D. preferred

Strong proficiency in R and Python for data analysis, visualization, and reproducible reporting — you write, own, and document your own code

Experience with cloud computing environments (AWS) for running scalable analyses

Demonstrated ability to execute analytical plans with precision and efficiency, managing multiple tasks without a drop in quality or documentation standards

Familiarity with NGS data types and standard processing pipelines — alignment, QC, coverage analysis, or equivalent

Clear and organized communicator — written documentation, results presentations, and cross-functional interactions alike

Comfortable working in a fast-paced startup environment, following defined protocols while contributing ideas for improvement

Nice to have:

Exposure to regulated environments — CLIA, CAP, FDA IVD, or design controls in any form

Experience with epigenomic data types — methylation, cfDNA, chromatin accessibility, or ChIP-seq

Familiarity with statistical thresholding or limit of detection frameworks for diagnostic applications

Experience contributing to SOPs, validation reports, or other regulated documentation