Computational Biologist, Principal Scientist - CVRI

 

At Bayer we’re visionaries, driven to solve the world’s toughest challenges and striving for a world where 'Health for all Hunger for none’ is no longer a dream, but a real possibility. We’re doing it with energy, curiosity and sheer dedication, always learning from unique perspectives of those around us, expanding our thinking, growing our capabilities and redefining ‘impossible’. There are so many reasons to join us. If you’re hungry to build a varied and meaningful career in a community of brilliant and diverse minds to make a real difference, there’s only one choice.

 

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.

 

WHO YOU ARE

 

Bayer seeks an incumbent who possesses the following:

 

Required Qualifications:

 

• 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 interdisc