Description review
Principal Scientist Algorithm Lead
NeoGenomics Laboratories · United States · back to the listing
HR standards
53/100
needs work
Title ↔ description
81/100
solid
Reads as
Data Scientist
86% confident
What this role officially is
data scientist — ESCO, the EU occupation classification
Data scientists find and interpret rich data sources, manage large amounts of data, merge data sources, ensure consistency of data-sets, and create visualisations to aid in understanding data. They build mathematical models using data, present and communicate data insights and findings to specialists and scientists in their team and if required, to a non-expert audience, and recommend ways to apply the data.
Also known as: data scientists, data engineer, research data scientist, data expert, data research scientist
How others title the same work
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What the listing never says
- 21 bullet points. Long requirement lists deter qualified candidates, who read them as hard gates. Scope clarity
- No pay range published. Candidates cannot tell whether applying is worth their time. Pay transparency
The listing, marked up
Nothing in the wording of this listing tripped a check. The scores above still judge how complete and coherent it is.
Are you motivated to participate in a dynamic, multi-tasking environment? Do you want to join a company that invests in its employees? Are you seeking a position where you can use your skills while continuing to be challenged and learn? Then we encourage you to dive deeper into this opportunity.
We believe in career development and empowering our employees. Not only do we provide career coaches internally, but we offer many training opportunities to expand your knowledge base! We have highly competitive benefits with a variety HMO and PPO options. We have company 401k match along with an Employee Stock Purchase Program. We have tuition reimbursement, leadership development, and even start employees off with 16 days of paid time off plus holidays. We offer wellness courses and have highly engaged employee resource groups. Come join the Neo team and be part of our amazing World Class Culture!
NeoGenomics is looking for a Principal Scientist Algorithm Lead – Clinical NGS Diagnostics who wants to learn to continue to learn in order to allow our company to grow. This is a remote position.
Now that you know what we're looking for in talent, let us tell you why you'd want to work at NeoGenomics:
As an employer, we promise to provide you with a purpose driven mission in which you have the opportunity to save lives by improving patient care through the exceptional work you perform. Together, we will become the world's leading cancer reference laboratory.
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
We believe in career development and empowering our employees. Not only do we provide career coaches internally, but we offer many training opportunities to expand your knowledge base! We have highly competitive benefits with a variety HMO and PPO options. We have company 401k match along with an Employee Stock Purchase Program. We have tuition reimbursement, leadership development, and even start employees off with 16 days of paid time off plus holidays. We offer wellness courses and have highly engaged employee resource groups. Come join the Neo team and be part of our amazing World Class Culture!
NeoGenomics is looking for a Principal Scientist Algorithm Lead – Clinical NGS Diagnostics who wants to learn to continue to learn in order to allow our company to grow. This is a remote position.
Now that you know what we're looking for in talent, let us tell you why you'd want to work at NeoGenomics:
As an employer, we promise to provide you with a purpose driven mission in which you have the opportunity to save lives by improving patient care through the exceptional work you perform. Together, we will become the world's leading cancer reference laboratory.
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