Description
Apply software engineering, data science, and biological knowledge to design, build, and validate bioinformatics tools, pipelines, and data systems for research and clinical use. Develop algorithms and models for omics data; manage large-scale datasets and cloud/HPC infrastructure; ensure data quality, security, compliance, and reproducibility; and translate results into actionable insights for scientists and clinicians.
- • Evaluate the accuracy, efficiency, and robustness of bioinformatics pipelines and algorithms.
- • Advise researchers, clinicians, or lab managers on sequencing strategies, data acquisition, and compute/storage needs.
- • Research and prototype novel algorithms, data structures, or machine learning approaches for omics analysis.
- • Develop computational models or simulations of biological networks and systems.
- • Collaborate with biologists, chemists, and clinicians to define requirements and interpret results.
- • Design, implement, and maintain software for sequence analysis, variant calling, expression quantification, and metagenomics.
- • Adapt or build databases, LIMS/ELN integrations, and APIs for biomedical data.
- • Analyze clinical genomics workflows to forecast diagnostic yield, turnaround time, and costs.
- • Conduct training and create tutorials to educate users on tools, pipelines, and best practices.
- • Write SOPs, QC protocols, and runbooks for data processing, validation, and incident response.
- • Collaborate with quality and regulatory teams to produce validation reports and ensure compliance (e.g., CLIA, CAP, HIPAA).
- • Work with vendors and IT to evaluate and integrate sequencing platforms, lab automation, and cloud services.
- • Ensure compatibility between wet-lab assays and downstream computational analysis; define data and metadata standards.
- • Consult on experimental design, sample size, and power analyses for omics studies.
- • Design follow-up wet-lab or in silico experiments based on computational findings.
- • Build and maintain scalable, reproducible pipelines using workflow engines and containers (e.g., Nextflow, Snakemake, Docker).
- • Benchmark tools with reference datasets and report sensitivity, specificity, and runtime metrics.
- • Develop and maintain statistical models, classifiers, and predictive biomarkers.
- • Lead process improvement initiatives for data ingestion, quality control, and release.
- • Maintain and curate datasets, metadata, ontologies, and data provenance; enforce FAIR data principles.
- • Manage engineers or analysts by setting priorities, schedules, and budgets.
- • Prepare roadmaps and capital plans for HPC or cloud infrastructure and data platforms.
- • Prepare technical reports, data summaries, manuscripts, and documentation for audits or regulatory submissions.
- • Stay current with literature and standards (e.g., GA4GH, HL7 FHIR) and evaluate emerging technologies.
- • Recommend algorithms, reference data, and compute architectures based on benchmarking results.
- • Review and optimize pipelines for performance, scalability, cost, and reproducibility.
- • Implement CI/CD, automated testing, and monitoring for bioinformatics software and pipelines.
- • Develop visualization dashboards and reports to communicate results to stakeholders.
- • Implement data security, access controls, encryption, and de-identification in line with policy.
- • Integrate structured results with clinical systems or EHRs to enable decision support.
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O*NET occupational data (work activities, skills, knowledge).
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Last reviewed: Jan 2026
