Description
Apply computational, statistical, and software engineering methods to analyze genomic and other omics data, uncover the genetic basis of traits at molecular, organism, and population scales, and support research and clinical interpretation. Develop algorithms, databases, and scalable pipelines to produce accurate, reproducible results.
- • Develop, validate, and maintain bioinformatics pipelines for sequencing and other omics data.
- • Design and manage genetics databases, schemas, and metadata standards.
- • Build and apply statistical and machine learning models for genetic data analysis.
- • Perform quality control, alignment, variant calling, and annotation on high-throughput sequencing data.
- • Integrate multi-omics datasets to study gene regulation, protein interactions, and metabolic networks.
- • Design study workflows, cohorts, and sampling strategies; perform power and population structure analyses.
- • Conduct population and family-based analyses, including pedigree, linkage, and association studies.
- • Interpret and review computational genetic results for research or clinical reporting.
- • Collaborate with biologists, clinicians, and lab scientists to plan analyses and validate findings.
- • Work with IT to develop and optimize data processing applications and scalable HPC or cloud infrastructure.
- • Develop protocols and standards for reproducible research, version control, and data provenance.
- • Evaluate new algorithms, software, and reference resources; benchmark and optimize performance.
- • Search and synthesize scientific literature to select and adapt analysis methods.
- • Prepare publications, presentations, and grant proposals; present results at conferences.
- • Train and mentor students and staff in computational genetics tools and methods.
- • Ensure data governance, privacy, and security compliance for genomic datasets.
- • Establish and monitor data quality metrics and dashboards for ongoing projects.
- • Supervise analysts and coordinate multi-disciplinary project teams.
- • Develop visualization tools and reports to communicate genetic findings to stakeholders.
- • Create and maintain containers and workflow definitions for portable, scalable analyses.
- • Implement pipelines for structural variant, copy-number, and expression analyses.
- • Perform statistical analyses to estimate heritability, selection, and demographic history.
- • Support clinical variant interpretation using databases, in silico predictors, and ACMG guidelines.
- • Maintain detailed electronic records, documentation, and metadata for all analyses.
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O*NET occupational data (work activities, skills, knowledge).
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Last reviewed: Jan 2026
