Bioinformatics Scientist / Senior Bioinformatics Scientist — cfDNA-Based Cancer Screening

Position Summary
We are seeking a highly motivated Bioinformatics Scientist or Senior Bioinformatics Scientist to develop, optimize, validate, and maintain bioinformatics pipelines for a cell-free DNA (cfDNA)-based colorectal cancer (CRC) screening assay. The successful candidate will work at the interface of computational biology, next-generation sequencing (NGS), molecular diagnostics, and statistical modeling. This individual will be responsible for transforming raw sequencing data into reliable analytical outputs and developing robust computational methods to identify cancer-associated molecular signals in plasma-derived cfDNA. The role will support analytical validation, algorithm development, assay performance optimization, and preparation for clinical and regulatory submissions. The candidate will collaborate closely with molecular biology, assay development, laboratory automation, clinical, quality, and regulatory teams to establish a reproducible, scalable, and well-documented bioinformatics workflow.
Key Responsibilities
1. NGS Bioinformatics Pipeline Development
Develop, optimize, and maintain end-to-end bioinformatics pipelines for cfDNA-based targeted sequencing assays.
Process and analyze raw sequencing data, including FASTQ, BAM/CRAM, and VCF or other assay-specific intermediate files.
Implement quality control metrics for sequencing quality, mapping, coverage, library complexity, molecular error rates, and sample-level performance.
Develop methods for low-frequency variant detection, error suppression, background-noise characterization, and identification of cancer-associated molecular features, as appropriate to the assay design.
Optimize computational workflows for throughput, reproducibility, scalability, and efficient processing of high-volume sequencing data.
Integrate bioinformatics outputs with laboratory information management systems (LIMS), automated laboratory workflows, and downstream reporting systems as needed.
2. Algorithm and Biomarker Development
Develop and evaluate computational methods to identify cancer-associated signals in cfDNA, including genomic alterations and other molecular features supported by the assay design.
Collaborate with molecular biologists and assay scientists to investigate biological and technical sources of background noise.
Develop statistical models and classification algorithms to distinguish cancer-associated signals from non-cancer background.
Evaluate feature selection, model training, cross-validation, threshold optimization, and generalizability using appropriate study designs.
Assess the effects of cfDNA input, sequencing depth, tumor fraction, sample quality, and pre-analytical variables on algorithm performance.
Maintain clear separation between model development, parameter tuning, and independent performance evaluation to minimize overfitting and data leakage.
3. Bioinformatics Analytical Validation
Design and execute bioinformatics validation studies in collaboration with assay development and analytical validation teams.
Establish and document software and algorithm performance characteristics, including:
Analytical accuracy and reproducibility.
Detection performance across relevant signal levels.
False-positive and false-negative analytical calls.
Robustness to sequencing depth, input variability, and technical noise.
Sample-level quality control and failure criteria.
Computational precision, deterministic behavior, and reproducibility across software versions.
Evaluate the impact of sequencing errors, contamination, sample mix-ups, batch effects, and other potential confounders.
Define acceptance criteria, validation datasets, statistical analysis plans, and validation reports before study execution.
Support investigations of discrepant results, pipeline failures, and unexpected assay performance.
4. Software Engineering and Computational Infrastructure
Develop maintainable, modular, and version-controlled bioinformatics software using Python, R, and/or other appropriate programming languages.
Implement workflow management using tools such as Nextflow, Snakemake, or WDL.
Use Git-based version control, automated testing, continuous integration, containers, and reproducible software environments.
Deploy workflows in cloud or high-performance computing environments, as appropriate.
Establish automated quality checks, audit-friendly execution logs, data lineage, version tracking, and monitoring.
Develop unit, integration, regression, and end-to-end tests to ensure reliable pipeline performance.
Collaborate with software engineering and IT teams to maintain secure and reliable computational infrastructure.
5. Clinical Performance and Translational Research Support
Support retrospective and prospective studies evaluating cfDNA-based cancer screening algorithms.
Collaborate with biostatisticians and clinical scientists to assess sensitivity, specificity, predictive values, and other clinical performance measures.
Support the development of independent training, tuning, and test datasets for algorithm development and evaluation.
Analyze discordant cases and investigate associations between molecular features, clinical findings, and reference-standard outcomes.
Support clinical study data analysis, technical documentation, and preparation of scientific publications and presentations.
6. Regulatory, Quality, and Documentation
Prepare technical documentation for analytical validation, software verification and validation, design controls, and regulatory submissions.
Work with Quality Assurance and Regulatory Affairs to ensure compliance with applicable requirements for clinical laboratory testing and in vitro diagnostic software.
Maintain traceability between software requirements, risk controls, test cases, validation evidence, and software releases.
Contribute to change-control procedures, risk assessments, deviation investigations, and corrective and preventive actions when applicable.
Support the development of controlled procedures for pipeline deployment, updates, maintenance, and release approval.
7. Cross-Functional Collaboration
Partner with assay development, molecular biology, laboratory operations, automation engineering, biostatistics, clinical development, and regulatory teams.
Translate biological and clinical questions into computational analyses and actionable technical requirements.
Communicate complex bioinformatics findings clearly to scientific and non-computational stakeholders.
Contribute to experimental design, assay optimization, troubleshooting, and strategic development of the cancer screening platform.
Required Qualifications
Ph.D. in Bioinformatics, Computational Biology, Genomics, Biostatistics, Computer Science, or a related discipline; alternatively, an M.S. with substantial relevant industry experience.
Strong experience analyzing NGS data, preferably from targeted sequencing assays or liquid biopsy applications.
Proficiency in Python and/or R, including experience developing reusable, well-tested analytical code.
Demonstrated understanding of NGS data processing, sequencing quality control, alignment, variant calling, and statistical analysis.
Experience with Linux/Unix environments, shell scripting, Git, and reproducible computational workflows.
Strong knowledge of statistical methods, experimental design, data quality assessment, and performance evaluation.
Ability to independently troubleshoot complex computational problems and communicate results across multidisciplinary teams.
Preferred Qualifications
Experience with cfDNA, circulating tumor DNA (ctDNA), liquid biopsy, or blood-based cancer screening assays.
Experience with ultra-low-frequency variant detection, molecular barcodes or unique molecular identifiers (UMIs), error-corrected sequencing, and sequencing background suppression.
Familiarity with machine learning, predictive modeling, classification algorithms, and evaluation of clinical biomarkers.
Experience developing or validating bioinformatics pipelines for oncology or clinical diagnostics.
Knowledge of analytical validation concepts, including limit of blank, limit of detection, precision, interference, reproducibility, and robustness.
Experience with Nextflow, Snakemake, Docker, Singularity/Apptainer, cloud computing, or high-performance computing.
Familiarity with regulated software development, design controls, software risk management, and relevant FDA and CLSI guidance.
Experience integrating sequencing workflows with LIMS, laboratory automation platforms, or clinical reporting systems.
Experience supporting clinical studies, regulatory submissions, or commercialization of molecular diagnostic assays.
Key Deliverables and Measures of Success
During the first 6–12 months, the successful candidate is expected to:
Establish or improve a reproducible end-to-end NGS bioinformatics pipeline for the cfDNA-based CRC screening assay.
Implement documented sample-level and sequencing-level quality control metrics and failure criteria.
Develop or optimize the assay-specific molecular signal detection and classification workflow.
Complete a bioinformatics verification and analytical validation plan with predefined acceptance criteria.
Demonstrate reproducibility, robustness, and traceability across representative datasets, runs, and software configurations.
Deliver controlled technical documentation, automated testing, and versioned software releases.
Collaborate with assay development and clinical teams to support performance optimization and future clinical validation.
Core Competencies
Scientific rigor and critical thinking.
Strong programming and statistical problem-solving skills.
Attention to detail and data integrity.
Ability to work with complex, high-dimensional biological datasets.
Effective cross-functional communication.
Ownership of technical deliverables and timelines.
Commitment to reproducible research, software quality, and patient safety.