Each day, the lives of more than 2 billion people across the globe are impacted by chronic diseases. Moreover, the economic burden on society of treating chronic disease is spinning out of control. Today, this dire situation appears unlikely to change as >95% of global healthcare costs are spent on treating rather than preventing chronic diseases. FL84, Inc. is a privately held early-stage company that is applying advanced biological and computational platforms to discover breakthroughs in detection of and intervention against the etiologies that drive progression from health to disease. Our goal is to leverage our proprietary platforms to disrupt the current approach of treating chronic disease too late. We endeavor to provide true health care rather than sick care to individuals that are at risk of progressing to disease.
FL84 was founded by Flagship Pioneering, an innovation enterprise dedicated to originating and developing first-in-category life sciences companies. Since Flagship’s founding in 2000, the firm has originated and fostered the development of nearly 100 scientific ventures resulting in $20+ billion in aggregate value, 500+ issued patents, and more than 50 clinical trials for novel therapeutic agents. Flagship-founded companies include Moderna Therapeutics, Rubius Therapeutics, Seres Therapeutics, Evelo Biosciences, and Indigo Agriculture.
We are seeking a computational biologist or bioinformatic expert who is enthusiastic about developing, learning and applying computational skills to understand complex biological systems and their relationship to clinical state changes. The candidate will design and implement novel approaches to handling biological data, initially focused on single-cell RNAseq but rapidly expanding to multiple types of biological and clinical data. An ideal candidate will pride themselves on their ability to craft scientifically logical stories out of complex data and convert them into executable experiments. The position will provide a unique opportunity to play a foundational role in the development of FL84’s preclinical platform.
- Develop and apply bioinformatics, computational biology, and machine learning tools to generate insights and hypotheses from high-dimensional molecular datasets, with an initial focus on time-series scRNA-seq and genomic data
- Work with FL84 team to develop and apply novel ML models on heterogenous biological data
- Identify and explore internal and external datasets to address questions critical to FL84’s core objectives and generate testable hypotheses
- Ideate on how to align time-series biological data with clinically relevant inflection points identified in electronic health records, clinical trials, or other sources
- Develop clear, intuitive visualizations and communicate analysis results via presentations to a multi-disciplinary audience
- Cultivate a data-centric company philosophy by helping to maintain best practices for software development, data management, and infrastructure
- Monitor and evaluate new and emerging technologies and models and identify opportunities for collaboration within Flagship Pioneering companies, academia, and third parties
- PhD or 5+ years of equivalent level of experience in quantitative biology. Ph.D. may be in Computational Biology, Bioinformatics, Computer Sciences, Applied Mathematics, Applied Physics, or related
- Practical programming and scripting skills, preferably in Python and R
- Breadth of experience applying deep learning (DL) models to biological data
- Motivated and team oriented, with an ability to thrive in an entrepreneurial and multidisciplinary environment
- Ability to independently lead and run research projects, while maintaining close communication with team members
- Excellent communication and presentation skills. Must be able to speak and ideate with multi-disciplinary team including biologists. Must be able to think independently, work collaboratively and contribute to an active intellectual environment
- Experience with biological, medical, and chemical data
- Experience with time series analysis, causal inference, domain adaptation, transfer learning, multi-modal deep learning, geometric deep learning (learning on graphs and/or manifolds)
- Experience working with reference biological databases and datasets (e.g., TCGA) is a strong plus
- Experience with CMAP, LINCS or other perturbagen (e.g., small molecule, CRISPT, etc.) induced transcriptomic databases
- Experience running genome wide association studies (GWAS)
- Familiarity with AWS, GCP, or similar cloud-computing services
- Ability to Google error messages and seek resolution from self-investigation