We are seeking a talented and motivated Computational Biologist to join our dynamic team. In this role, you will be responsible for designing, developing, and optimizing analytical pipelines for cutting-edge spatial multi-omics datasets. You will leverage your expertise in workflow management systems, particularly Nextflow, to create scalable and reproducible solutions for single-cell RNA sequencing (scRNA-Seq) and other complex data types. The ideal candidate will have a solid foundation in both bioinformatics and workflow engineering, with a passion for uncovering biological insights. You will collaborate closely with cross-functional teams of scientists and engineers to drive our research forward.
Responsibilities:
Design, implement, and optimize robust analysis pipelines for spatial transcriptomics and other multi-omics data using Nextflow.
Develop and apply advanced computational and statistical methods for the analysis and interpretation of scRNA-Seq data.
Collaborate with bench scientists and data scientists to translate experimental needs into scalable workflow solutions.
Ensure that all analysis workflows are well-documented, version-controlled, and reproducible.
Evaluate and implement novel algorithms and tools to stay at the forefront of spatial and single-cell analytics.
Present findings and contribute to scientific discussions within the team and the broader organization.
Experience:
Bachelor's or Master's degree in Bioinformatics, Computational Biology, Data Science, or a related field, with 3+ years of relevant experience.
Demonstrated experience building and maintaining bioinformatics pipelines using Nextflow.
Proficiency in both Python and R for data analysis.
Experience with the analysis of scRNA-Seq or other high-throughput sequencing data.
Strong understanding of statistical principles as applied to biological data.
Proficiency with Git and GitHub for version control.
Excellent problem-solving, communication, and teamwork skills.
Preferred Qualifications
PhD in a relevant field.
Direct experience with spatial multi-omics data analysis.
Background in immunology, oncology, or a related biological domain.
Experience with containerization technologies (e.g., Docker, Singularity) for creating reproducible analysis environments.
Familiarity with the AWS cloud computing environment.
Deep knowledge of common bioinformatics tools (e.g., Seurat, Scanpy, Bioconductor).
