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This listing scored 66/100, which is a C. It lost the most ground on pay transparency.
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$123k – $169k
That is the middle half of what comparable roles paid on this board over the last 90 days — 15 listings that did publish a figure, median $154k. It is not this employer's offer, and we have no idea what they pay. It is only what the rest of the market advertised.
Senior Full Time
Position Summary:
As thePrincipal Scientist Algorithm Lead you will provide end‑to‑end scientific and technical leadership for clinical‑grade NGS diagnostic algorithms, with a primary focus on oncology and liquid biopsy applications. This role owns algorithm design, analytical validation, design control, and regulatory readiness, with an emphasis on improving sensitivity, robustness, and reproducibility across complex variant classes.Responsibilities:
- Own the full lifecycle of clinical NGS algorithms under design control, including requirements definition, risk analysis, traceability to analytical claims, and design change impact assessment.
- Architect and lead automated analytical validation frameworks spanning accuracy, precision, sensitivity/LOD, specificity, linearity, and robustness for SNVs, indels, CNVs, structural variants, gene fusions, and RNA‑based assays.
- Define algorithm‑level error models, performance budgets, and acceptance criteria, driving systematic improvements in low‑VAF detection, background suppression, and assay‑specific artifact mitigation.
- Establish statistically rigorous approaches for truth set construction, reference materials, in silico mixing, and synthetic data generation to support scalable and reproducible validation.
- Serve as final technical authority on algorithm changes, including re‑validation scope, documentation strategy, and regulatory impact.
- Lead development and optimization of variant calling and signal extraction algorithms for DNA‑and RNA‑based assays, including ultra‑deep sequencing and challenging genomic regions.
- Develop and track NGS‑based quality control metrics at the read, molecule, sample, and assay levels (e.g., coverage, uniformity, duplication/UMI yield, error rates, contamination, noise profiles) to monitor analytical performance and stability.
- Apply probabilistic modeling, Bayesian inference, and machine learning to improve sensitivityand specificity while maintaining interpretability and regulatory defensibility.
- Lead algorithm development for solid tumor and hematologic malignancy profiling, including tissue and liquid biopsy use cases.
- Address challenges specific to low‑input DNA/RNA, fragmented cfDNA, and ultra‑low‑allele‑frequency variants.
- Translate algorithm behavior and QC performance into clear, testable analytical claims aligned with CLIA, CAP, FDA, NYDoH, CLSI, and MolDx expectations.
- Author and review algorithm components of validation reports, design history documentation, and regulatory submissions.
Education, Experience & Qualifications:
- PhD in Bioinformatics, Computational Biology, Computer Science, Statistics, or a related quantitative field.
- 8+ years of experience developing algorithms for clinical NGS diagnostics, ideally in oncology.
- Deep expertise in SNV/indel, CNV, SV, fusion, and RNA analysis, NGS QC metrics, statistical modeling, and analytical performance evaluation.
- Demonstrated leadership in analytical validation and regulatory submissions (CLIA, CAP, FDA, NYDoH, MolDx).
- Hands‑on experience applying AI/ML methods to NGS data or biomarker development.
- Expert programming skills in Python and R; strong understanding of workflow orchestration and validation automation.
- Strong publication or presentation record in computational genomics or NGS diagnostics.
- Experience building QC‑driven, highly automated validation pipelines with rigorous statistical controls.
- Familiarity with payer evidence and reimbursement considerations for molecular diagnostics.
Originally posted on Himalayas
Apply for this role Opens himalayas.app — the link as listed; we have not yet verified it is the employer's own page
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Where this listing came from
- 07 Oct 2026 Himalayas first sighting
Seen on 1 board over 0 days.