Biologist principal scientist
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Computational Biologist, Principal Scientist - CVRI The Computational Biologist is responsible for the evaluation of therapeutic hypotheses via integration and quantitative analyses of internal & external multi-omics data sources (DNA, RNA, proteomics, metabolomics, imaging, etc) for CardioVascular, renal and immunology Therapeutic Areas. Significant contributions to innovative research and development projects or collaborations, such as: identifying new targets with multi-omics evidence; assessing the link between targets and disease biology; proposing pharmacodynamic, efficacy or selection biomarkers; evaluating biomarker hypotheses from clinical trial specimens; assessing novel technologies; supporting therapeutic lifecycle management. Adopts and contributes to Best Practices for novel technologies, quantitative analyses, visualizations, and biological insight generation from multi-omics data sources. Your Tasks And Responsibilities The primary responsibilities of the Computational Biologist are to:- Evaluate and contextualize the molecular evidence supporting a therapeutic hypothesis with relevant internal and external multi-omics data (DNA, RNA, proteomics, metabolomics, imaging, etc);
- Contribute to Project Team scientific strategy and prosecution of critical path objectives, such as the identification of novel targets, biomarkers, or patient selection hypotheses. These contributions use state-of-the art computational biology methods to analyze large and complex human data sets;
- Deliver biological insights on therapeutic assets and disease biology understanding to Project Teams, who apply these insights towards stage-gate decisions, portfolio prioritization, and/or submissions to Health Authorities;
- Educate Project Teams on innovations in disease understanding in the CardioVascular, renal and immunology Therapeutic Areas;
- Develop, evaluate, implement, and apply Best Practices for the execution and validity of quantitative analyses, integration, visualizations, and interpretation of multi-omics data. Demonstrate a growth mindset towards updating Best Practices with the emergence of new experimental or computational technologies;
- Build cross-functional relationships to collaboratively partner across Pharma R&D and effectively network within disease understanding and data science communities of experts.
- PhD degree with post-graduation experience in Sciences (Bioinformatics, Computational Biology, Biology, Chemistry, Genetics, Computer Science, Informatics, or other related quantitative discipline) with very strong computational focus;
- Demonstrated experience in statistical analysis of DNA-seq, RNA-seq, proteomics, single-cell, or imaging datasets with proficiency in a programming environment (such as R or Python);
- Demonstrated hands-on experience in analysis of large scale proteomics, metabolomics datasets;
- Demonstrated peer-reviewed publications in computational biology;
- Expert level of proficiency to choose fit-for-purpose quantitative analyses;
- Strong analytical thinking and scientific rigor;
- Strong communication competencies to include presentations and delivery of complex quantitative analyses in a clear, concise, and actionable manner to broad audiences and key stakeholders across multiple functions or to external partners;
- Learning agility, highly self-motivated, with an aspiration of continuous improvement of oneself and others;
- Evidence of providing tactical and strategic guidance to the pharma portfolio or data science ecosystem;
- Demonstrated ability to propose, catalyze, and implement the development of interdisciplinary, systematic solutions that create business value;
- Capability to present even very complex issues clearly and convincingly to project teams or decision bodies. Good communication skills. Can manage a high degree of complexity;
- Demonstrates advanced business acumen, people and project leadership competencies, and technical expertise.
- 5+ years of post-graduation experience in Sciences (Bioinformatics, Computational Biology, Biology, Chemistry, Genetics, Computer Science, Informatics, or other related quantitative discipline) with very strong computational focus;
- 3+ years industry experience in drug development or biotech role with clear understanding of applying multi-omics data analyses to advance a portfolio of drug discovery or clinical development projects.
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