Principal Scientist Algorithm Lead

Principal Scientist Algorithm Lead at NeoGenomics Laboratories Principal Scientist Algorithm Lead at NeoGenomics Laboratories in Fort Myers, Florida Posted in 7 days ago.

Job Description:

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 the Principal Scientist Algorithm Lead you will provide endtoend scientific and technical leadership for clinicalgrade 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 RNAbased assays. Define algorithmlevel error models, performance budgets, and acceptance criteria, driving systematic improvements in lowVAF detection, background suppression, and assayspecific 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 revalidation scope, documentation strategy, and regulatory impact. Lead development and optimization of variant calling and signal extraction algorithms for DNA and RNAbased assays, including ultradeep sequencing and challenging genomic regions. Develop and track NGSbased 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 lowinput DNA/RNA, fragmented cfDNA, and ultralowallelefrequency 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). Handson 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 QCdriven, highly automated validation pipelines with rigorous statistical controls. Familiarity with payer evidence and reimbursement considerations for molecular diagnostics. Yes. Attaching proposed JD. Equal employment opportunity, including veterans and individuals with disabilities. PI284152297