Job Summary:

- 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.
- Solid 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).

📌 Computational Biologist (India)
🏢 System Soft Technologies
📍 India