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 computational biologist driven to leverage single-cell genomics data to reveal biological mechanisms of health and disease. The candidate will participate in the statistical design and analysis of genomic experiments (e.g. single-cell RNA-seq, ATAC-seq, CRISPR screens, etc.) in collaboration with wet-lab biologists. The role requires the ability to interpret experiments that deliver testable hypotheses that integrate clinical and biological endpoints using cutting edge methods and technologies. The candidate should have a solid foundation in applied statistics, deep generative models, computational biology, molecular biology paired with a strong work ethic and the ability to work independently and in highly matrixed teams. 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 to support the development of novel therapeutics.
- Computational analysis of large, complex, single cell genomics datasets from in vitro cellular model systems
- Deliver testable hypotheses/insights from complex high-dimensional data to inform target selection
- Linking results and insights between internal and public data, as well as orthogonal data such as human genetics.
- 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 single-cell RNA-seq data and analyses (experience with ATAC-seq, CRISPR, PPI, etc. a plus)
- Strong knowledge of applied statistics and machine learning (in particular deep generative models)
- Strong statistical and scripting programming skills (Python/R/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 high-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.