The latest Flagship Pioneering company has been founded through its venture creation engine, where companies such as Moderna Therapeutics, Rubius Therapeutics, and Evelo Therapeutics were conceived and created. Since Flagship’s founding in 2000, the firm has originated and fostered the development of nearly 100 scientific ventures resulting in $19+ billion in aggregate value, 500+ issued patents, and more than 50 clinical trials for novel therapeutic agents.
The new company is dedicated to turning causal human genetics and biology into new, differentiated disease modifying therapies to improve the lives of patients across a broad range of complex diseases.
We are seeking a creative, motivated computational biologist driven to leverage and integrate multi-omic data to reveal biological mechanisms of health and disease. The candidate will participate in the statistical design and analysis of genomic experiments (i.e. bulk RNA-seq, single-cell RNA-seq, ATAC-seq, CRISPR screens, etc.) in collaboration with wet-lab biologists. The role requires the ability to design and interpret experiments that deliver testable hypotheses that integrate clinical and biological endpoints. The candidate should have a solid foundation in applied statistics (including machine learning and graph theory), computational biology, molecular biology, a strong work ethic and the ability to work independently and in highly matrixed teams. Preference will be given to candidates with experience in identifying new mechanisms in complex cellular models. The candidate will leverage publicly available data and integrate with internally generated data. This is an exciting and interdisciplinary role that will collaborate with statistical geneticists, biomedical informaticists and wet-lab biologists. This computational biology position with the Computational Biology & Data Science team provides a unique opportunity to harness the information from human genetics, genomics, and advanced wet-lab techniques to support the development of novel therapeutics.
- Computational analysis of high-dimensional RNA & protein datasets from cellular systems and patient samples
- Deliver testable hypotheses/insights from complex multi-dimensional data to inform target selection
- Identify and validate approaches to improve quality and efficiency of hypothesis generation from model systems
- Maintain awareness of emerging methods in computational biology and applications for novel omics technologies
- Provide ad-hoc bioinformatics support to cross-disciplinary project teams
- Reporting results to scientific team and management.
- PhD in Bioinformatics, Biostatistics, Computer Science, Computational Biology, Genetics, Mathematics, Physics, Statistics or other related discipline
- 2-3 years post PhD experience applying quantitative approaches to solve biological problems, ideally in a pharmaceutical or biotech context.
- Knowledge and experience of multiple NGS & genomics data and statistical analyses (e.g. bulk RNA-seq, single-cell RNA-seq, ATAC-seq, CRISPR, PPI, etc.)
- Strong statistical and scripting programming skills (R/Python/etc.)
- Strong knowledge of applied statistics including machine learning and graph/network theory
- Experience analyzing and integrating large publicly available data (e.g. GTEx, ENCODE, etc.)
- Knowledge of molecular biology, neuroscience experience a plus
- Demonstrated experience in design and interpretation of in vivo and/or in vitro biological experiments.
- Demonstrated expertise in delivering insights/hypotheses from complex multi-dimensional biological data
- Demonstrated ability to collaborate with biologists to communicate results and discuss follow-up experiments
- Exceptional communication skills (oral and written) as demonstrated by publications & presentations.