Computational Biologist
Qualifications:
Education Minimum Requirements • Ph.D. in Bioinformatics, Computational Biology, Computer Science, Genetics, Physics, or related field, with hands on experiences in NGS data analysis Required Experience and Skills • Hands on experience in performing analysis and interpreting biology with large-scale omics datasets including transcriptomics, single cell RNA-Seq is required, knowledge and experience in other omics modalities are preferred, including proteomics, metabolomics, lipidomics and epigenetics. • Proficiency in at least one programming language, such as R, Python, Perl or MatLab • Capable of prioritizing projects and providing high quality deliverables on time • Demonstrated ability to provide technical support, perform translational research, and contribute to cross-functional projects. • Effective written and verbal communication skills Preferred Experience and Skills • Experiences in machine learning and AI • Capability of integrating multiple resources to strengthen research comprehensiveness. • Experience in applying computational methods to problems in cardiovascular and metabolic disease. • Familiarity with public databases and repositories of DNA and RNA profiling data • Strong publication record Note: Onsite candidates are preferred. Open to remote candidates if they meet all the required skills and experience and are right fit for the role.
Responsibilities:
We are seeking a motivated Computational Biologist with hands on experience to join our team dedicated to advancing our Cardiometabolic Disease (CMD) portfolio. As a scientist in CMD, you will: • Be part of creative and enthusiastic teams working on target identification and validation (TIDVAL) for heart failure, NASH, fibrosis, inflammation, obesity, retinal diseases, and vascular disease • Work with dynamic cross-functional matrixed teams to support discovery activities. • Be responsible to adapt pipelines and create data analysis solutions to analyze large scale omics data (transcriptomics, genomic, genetic, metabolomic, proteomic) for TIDVAL. • Work with in-house, open-source and/or commercially available platforms for the processing and analyzing large datasets. • Engage and collaborate with wet lab scientists on experimental design, data analysis and interpretation, and mechanistic understanding of target biology. • Have a proven track record across a wide range of computational biological methods, including but not limited to: next generation sequence data analysis, especially bulk RNAseq and scRNAseq, and data mining • Collaborative mindset with strong communication and presentation skills