Description
Apply bioinformatics methods to process and analyze genomic and other omics data for research, clinical, and product teams. Build, run, and optimize reproducible pipelines; curate and manage datasets and databases; generate statistical summaries and reports; and support tool selection and integration. Customize workflows, ensure data quality and compliance, and communicate findings to stakeholders.
- • Recommend process and system improvements to enhance bioinformatics operations.
- • Stay current on new methods, instrumentation, and software via literature and conferences.
- • Coordinate with research, clinical, product, and operations teams to scope analyses.
- • Partner with software engineers to operationalize and optimize bioinformatics pipelines.
- • Evaluate and test new and updated bioinformatics tools and reference datasets.
- • Provide statistical analyses and computational workflows for genetic and functional studies.
- • Prepare cohort- and sample-level genomic summary statistics and QC metrics.
- • Train users in the selection and use of bioinformatics tools and portals.
- • Improve usability of internal bioinformatics dashboards, databases, and data portals.
- • Coordinate with technicians and IT staff to support data processing and pipeline runs.
- • Customize scripts and workflows to meet specific project requirements.
- • Build and maintain data models, metadata, and curated databases.
- • Develop and maintain simple web-based utilities or interfaces for data access.
- • Implement and apply established algorithms, including machine learning where appropriate.
- • Adapt and integrate analytical approaches to meet evolving research goals.
- • Compile, clean, and harmonize data for gene expression profiling, genome annotation, or structural analyses.
- • Document methods and communicate results in reports, presentations, or publications.
- • Retrieve, manage, and integrate data from public, commercial, and proprietary omics databases.
- • Consult with investigators to define computational strategies and troubleshoot analyses.
- • Analyze large molecular datasets (e.g., NGS, transcriptomics, proteomics) for basic, translational, or clinical studies.
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Last reviewed: Jan 2026
