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Data-Driven Computational Scientist
2 months ago
Planet Pharma is seeking a highly skilled Computational Scientist to join our Cancer Immunogenomics team in the Computational Biology and Translation Pillar of R&D Computational Sciences. As a key member of our team, you will play a crucial role in analyzing large-scale single-cell and bulk RNA, TCR, ADT, and ATAC-seq datasets to drive scientific hypotheses and deliver novel insights.
Key Responsibilities- Collaborate with Scientists in Oncology: Work closely with our oncology team to analyze complex datasets, identify patterns, and develop actionable insights to propel our pipeline and clinical research goals.
- Develop Analytical Approaches: Design and implement advanced computational methods, including machine learning, statistical modeling, and bioinformatics pipelines, to integrate and interpret large-scale datasets.
- Coordinate Dataset Processing: Oversee the standardization and analysis-readiness of newly incoming datasets, ensuring seamless integration into our research workflow.
- Document Code and Analysis: Maintain accurate documentation of code, analysis processes, and findings, including standardization of analytical workflows.
- Present Findings: Effectively communicate research results to the Cancer Immunogenomics group and oncology collaborators.
- PhD or Master's in Computational Biology or Related Field: Possess a strong background in computational biology, bioinformatics, or a related field, with a proven track record in single-cell data analysis.
- Expertise in Immuno-Oncology and T Cell Biology: Demonstrate in-depth knowledge of immuno-oncology, innate immunity, and T cell biology, with experience in advanced analytical techniques.
- Programming Skills: Proficiency in R, Python, and shell scripting, with experience working with Git and high-performance computing.
- Communication and Teamwork: Excellent communication skills, with the ability to work effectively as part of a team and produce high-quality analysis results with minimal supervision.
