FL84, Inc. is a privately held early-stage company that is leveraging advanced biological and computational tools to develop breakthroughs in both detection and intervention needed to secure our health. More specifically, FL84 is pairing various 'omics technologies with deep analyses of clinical data to map the underlying transition from health 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, Evelo Biosciences, and Indigo Agriculture.
We are seeking an individual who is enthusiastic about developing, learning and applying computational skills to understand complex clinical trajectories from healthy to disease. The role will include significant hands-on analyses of clinical data such as electronic health records, insurance claims, laboratory measurements, images, and genetic data. The candidate will work closely with the computational biology team to develop insights and novel prediction platforms for different stages of the journey from health to disease. An ideal candidate will pride themselves on their interest and potential in crafting scientifically explainable stories out of complex data and convert them into executable clinical study designs. The position will provide a unique opportunity to play a foundational role in the development of FL84’s preclinical platform.
- Work with FL84 team to develop and apply novel ML models (e.g., disease progression models, among others) on clinical data (EHR, insurance claims, labs, imaging, narrative notes, etc.)
- Develop prediction algorithms to forecast major changes in individuals' health with reasonably high success
- Extract information from literature and large public databases for predictive model refinement and real-world application
- Ideate on how to align time-series biological data with clinically relevant inflection points identified in electronic health records, clinical trials, or other sources
- Identify and explore internal and external clinical datasets to address questions critical to FL84’s core objectives and generate testable hypotheses
- Develop clear, intuitive visualizations and communicate analysis results via presentations to a multi-disciplinary audience
- Cultivate a data-centric and process-oriented company philosophy by helping to maintain best practices for software development, data management, and infrastructure
- M.S. or equivalent level of experience in applying machine learning/artificial intelligence to large-scale clinical data. M.S. may be in Biomedical Informatics, Bioinformatics, Machine Learning, Statistics, Computer Science, Data Science, Mathematics, or similar technical fields
- Demonstrated experience applying traditional and deep learning models to electronic health record or administrative claims data
- Fluency in Python and experience with R and SQL
- Familiarity with AWS or similar cloud-computing services
- Ability to thrive in an entrepreneurial and multidisciplinary environment
- Experience in one or more of the following areas/topics: predictive models (classification or regression); regularization; recurrent, convolutional, graph, and/or transformer neural networks; and model explainability
- Experience running genome wide association studies (GWAS)
- Ability to Google error messages and seek resolution from self-investigation