Principal Scientist, Computational Biology Oncology

Bayer•Cambridge, MA

9h•Onsite

About The Position

Bayer is a biopharmaceutical company committed to applying its scientific leadership in Oncology to transform the lives of patients with cancer. We are growing rapidly with an active research and discovery pipeline. We are seeking an agile innovator who brings excitement for drug discovery in cancer biology, as well as a passion for the cultural transformation of Dynamic Shared Ownership. The Principal Scientist, Computational Biology is responsible for the evaluation of therapeutic hypotheses via integration and quantitative analyses of internal & external multi-omics data sources (DNA, RNA, CRISPR, ChIP-seq, methylation, and proteomics). Significant contributions to innovative research and development projects or collaborations, such as: identifying new targets with multi-omics evidence for a defined patient population; assessing the link between targets and disease biology; proposing indication selection and expansion hypotheses; proposing pharmacodynamic biomarkers; evaluating biomarker hypotheses from clinical trial specimens; assessing novel technologies; supporting therapeutic lifecycle management. You will adopt and contribute to Best Practices for novel technologies, quantitative analyses, visualizations, and biological insight generation from multi-omics data sources.

Requirements

PhD degree in Life Sciences (Bioinformatics, Computational Biology, Biology, Genetics, Molecular Biology, or other related 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 peer-reviewed publications in computational biology;
Expert level of proficiency to choose fit-for-purpose quantitative analyses;
Demonstrated ability to propose, catalyze, and implement the development of interdisciplinary, systematic solutions that create business value;
Evidence of providing tactical and strategic guidance to the pharma portfolio or data science ecosystem;
Strong communication competencies, including capability to present even very complex issues clearly and convincingly to project teams or decision bodies;
Demonstrates advanced business acumen, people and project leadership competencies, and technical expertise;
Can manage a high degree of organizational and technical complexity;
Learning agility, highly self-motivated, with an aspiration of continuous improvement of oneself and others.

Nice To Haves

5+ 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.

Responsibilities

Evaluate and contextualize the molecular evidence supporting a therapeutic hypothesis with relevant internal and external multi-omics data (DNA, RNA, CRISPR, ChIP-seq, methylation, and proteomics);
Design, execute, and interpret complex quantitative analyses to address precision drug development criteria. Examples of precision drug development criteria include RAISE dimensions, translational science strategy, proof-of-mechanism, and lifecycle management;
Contribute to Product 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 data sets;
Deliver biological insights on therapeutic assets and disease biology understanding to Product Teams, who apply these insights towards stage-gate decisions, portfolio prioritization, and/or submissions to Health Authorities;
Educate Product Teams on innovations in disease understanding, especially molecular mechanisms of tumor vulnerabilities that may represent unmet medical need;
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.