We are a research‑driven group working with large‑scale genomic and related biomedical datasets to support studies in areas such as rare disease, oncology, infectious disease, and neurology. Our work focuses on developing and applying computational methods that help collaborators interpret complex molecular data and generate results that can inform research and, where applicable, clinical decision‑making.
The team includes scientists, analysts, and software professionals who collaborate closely with partners in academic, clinical, and industry settings.
Position Overview
The Bioinformatics Analyst will be responsible for developing and running data analysis workflows, performing quality control, and summarizing results from next‑generation sequencing and other omics data. The role combines hands‑on data analysis with the design and maintenance of reproducible computational pipelines.
This position is suited to someone who enjoys working directly with data, building robust workflows, and communicating findings to a variety of stakeholders.
Key Responsibilities
Process and analyze genomic and other omics datasets (for example, whole‑genome, whole‑exome, RNA‑seq, or similar assays), including alignment, quality assessment, variant detection, and annotation.
Develop, document, and maintain reproducible analysis pipelines using modern workflow or pipeline tools and scripting languages.
Implement best practices for data quality control, including monitoring run performance, detecting technical issues, and proposing corrective actions.
Integrate data from multiple sources, harmonize formats and metadata, and prepare analysis‑ready datasets.
Work with common bioinformatics tools and file formats (e.g., FASTQ, BAM/CRAM, VCF, BED, GFF/GTF) in a Unix/Linux environment.
Develop and execute exploratory analyses, including cohort‑level summaries, visualization of key metrics, and interpretation of variant and gene‑level results.
Prepare clear, well‑structured reports, figures, and presentation materials describing methods, assumptions, and findings for collaborators with diverse backgrounds.
Contribute to the evaluation and adoption of new algorithms, tools, and workflows in bioinformatics and data analysis.
Collaborate with other team members on study design, analysis plans, timelines, and prioritization of tasks.
Requirements
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.
Preferred 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.
Working Style
Comfortable working in a collaborative environment with researchers, analysts, and software professionals.
Able to estimate effort, communicate progress, and flag risks or issues early.
Curious and willing to learn new analytical methods, tools, and technologies.
