Our Biotechnology company located in Watertown, MA is growing their Computational Biology team and looking for Scientist/Sr. Scientist join! They are pioneering a transformational new modality called targeted protein degradation (TPD), leveraging the body’s own protein recycling machinery, the Ubiquitin Proteasome System (UPS), to degrade disease-causing proteins and pursue disease targets long considered undruggable. They are looking for Computational Biology Scientists with experience within the Oncology and/or Immunology space and can provide NGS and -Omics data analysis support to their internal clients. Responsibilities include but not limited to:
- Perform large-scale integrated analyses of public and proprietary datasets for target identification and validation.
- Collaborate with project team partners to design and execute genomics, transcriptomics and proteomics experiments to elucidate mechanism of action of small molecules and identify biomarkers for monitoring and predicting response.
- Process raw data generated by these experiments using in-house computational pipelines.
- Integrate and analyze these datasets and present the findings in cross-functional project team meetings.
- Prepare publication-quality visualizations and document analyses for external audiences.
- Support and maintain data processing and analysis pipelines.
- PhD in Computational Biology, Bioinformatics, Computer Science, or other related field with 3+ years of relevant experience. Master’s degree with 5+ years of industry experience will be considered.
- Extensive experience with analysis of high-dimensional biological datasets such as genomics, transcriptomics, or proteomics datasets.
- Experience developing algorithms for analysis of large biological datasets with Python and R
- Familiarity with Unix-based high-performance computing cluster environments such as SLURM or SGE. Experience with cloud computing such as Amazon Web Services or Google Cloud a plus.
- Knowledge of signaling pathways, protein homeostasis, and regulation of gene expression in cancer biology highly desired.