Associate Scientist / Scientist I, Computational Test Development
On-siteBoston, Massachusetts, United States
Job Summary
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 by designing analytical approaches and returning well-documented results. Develop and apply statistical approaches to threshold setting and performance boundary definition, supporting limit of detection and analytical range characterization. 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. Present analytical findings clearly in team meetings and cross-functional settings.
Required Qualifications
- MSc in computational biology, bioinformatics, biostatistics, or a closely related quantitative field
- 3–4 years of industry or post-graduate research experience in a data-intensive biological or biomedical setting
- Strong proficiency in R and Python for data analysis, visualization, and reproducible reporting
- Experience with cloud computing environments (AWS) for running scalable analyses
- 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
Desired Qualifications
- Ph.D.
- 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
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