Principal Scientist Algorithm Lead

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.
• 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 sensitivity and 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.

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.