[Hiring] Computational Biologist @System Soft Technologies

Role Description

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.

• 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.

Qualifications

• 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.

Requirements

• PhD in a relevant field (preferred).
• Direct experience with spatial multi-omics data analysis (preferred).
• Background in immunology, oncology, or a related biological domain (preferred).
• Experience with containerization technologies (e.g., Docker, Singularity) for creating reproducible analysis environments (preferred).
• Familiarity with the AWS cloud computing environment (preferred).
• Deep knowledge of common bioinformatics tools (e.g., Seurat, Scanpy, Bioconductor) (preferred).