Bioinformatics Analyst
RemoteUnited States
Job Summary
Process and analyze genomic datasets including alignment, quality assessment, variant detection, and annotation. Develop, document, and maintain reproducible analysis pipelines using modern workflow tools and scripting languages. Implement best practices for data quality control, monitor run performance, and detect technical issues. Integrate data from multiple sources, harmonize formats, and prepare analysis-ready datasets in a Unix/Linux environment. Execute exploratory analyses, generate cohort-level summaries, and create clear reports and visualizations for diverse stakeholders. Contribute to the evaluation of new algorithms and collaborate on study design and task prioritization.
Required Qualifications
- Graduate degree or equivalent experience in Bioinformatics, Computational Biology, Genomics, Computer Science, Statistics, or a related field.
- Practical experience with next‐generation sequencing data analysis (such as WGS, WES, or RNA‐seq), including quality control, alignment, and variant or expression analysis.
- Proficiency in at least one scripting language commonly used in bioinformatics (e.g., Python or R), and familiarity with relevant scientific or data‐analysis libraries.
- Experience working in Unix/Linux environments, including shell scripting and command‐line tools.
- Familiarity with standard genomics file formats and commonly used open‐source tools for sequence data processing and variant analysis.
- Exposure to workflow or pipeline management tools (such as Nextflow, Snakemake, CWL, WDL, or comparable systems), and an understanding of reproducible analysis practices.
- Strong organizational skills, attention to detail, and the ability to manage multiple analysis tasks in parallel while meeting agreed timelines.
- Clear written and verbal communication skills, including the ability to describe analytical approaches and results to non‐specialists.
Desired Qualifications
- Experience with clinical or population‐based genomic datasets in any disease area.
- Familiarity with high‐performance or distributed computing environments used for computational biology workloads.
- Experience building and maintaining ETL (extract–transform–load) workflows and working with relational or NoSQL databases.
- Background in statistics, statistical genetics, or related quantitative disciplines.
- Contributions to shared code bases or open‐source projects in bioinformatics or data analysis.
- Experience generating visualizations or dashboards for scientific data using tools such as R Shiny, Plotly, Dash, or similar frameworks.
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