We are seeking a motivated Computational Biologist with hands on experiences to join our team dedicated to advance 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, 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, mathematical modeling, machine learning, and data mining.
• Collaborative mindset with strong communication and presentation skills
Minimum Requirements: Ph.D. in Bioinformatics, Computational Biology, Computer Science, Genetics, Physics, or related field, with hands on experiences in NGS data analysis. Minimum Experience: 1 year of relevant experience.
Required Experience and Skills:
• Hands on experience in performing analysis and interpreting biology with large-scale omics datasets including genetics, transcriptomics, single cell RNA-Seq, proteomics, metabolomics is required.
• 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