Description
Apply computational biology, statistics, and software engineering to analyze and interpret genomic and other omics data for research, diagnostics, and product development. Design, implement, and maintain algorithms, databases, and reproducible pipelines that process biological data and translate results into actionable insights.
- • Analyze large-scale sequencing, transcriptomics, proteomics, or metabolomics datasets for research or clinical use.
- • Design, implement, and optimize reproducible bioinformatics pipelines and workflows.
- • Develop and maintain data models, schemas, and databases for biological data.
- • Build or customize software and scripts to meet project needs.
- • Evaluate and benchmark new and updated bioinformatics tools.
- • Provide statistical methods and computational tools for genetic analysis, gene expression, and functional annotation.
- • Perform quality control, normalization, and integration of multi-omics datasets.
- • Annotate genomes, variants, and gene features using public and proprietary resources.
- • Generate summary statistics, dashboards, and visualizations for human and other genomes.
- • Collaborate with researchers and cross-functional teams to define requirements and computational strategies.
- • Support product, clinical, or operations teams to advance development and improvement efforts.
- • Improve user experience of bioinformatics tools, web portals, and data APIs.
- • Manage and curate data across public, commercial, and proprietary databases and ensure FAIR practices.
- • Create and apply machine learning, statistical modeling, and graph algorithms where appropriate.
- • Stay current with new assays, instrumentation, databases, and software through literature and conferences.
- • Document methods, pipelines, and results; communicate findings via reports and presentations.
- • Train scientists and staff in the selection and use of bioinformatics tools and workflows.
- • Lead or mentor technicians and IT staff supporting bioinformatics operations.
- • Recommend and implement new systems, standards, or processes to improve data operations.
- • Use HPC and cloud environments, containers, and version control to ensure scalability, security, and reproducibility.
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Last reviewed: Jan 2026
