Scientist/Sr. Scientist, Health Data Science

Scientist/Sr. Scientist, Health Data Science

 

Company Summary:

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. Flagship Pioneering conceives, creates, resources, and develops first-in-category life sciences companies to transform human health and sustainability. Since its launch in 2000, the firm has applied a unique hypothesis-driven innovation process to originate and foster more than 100 scientific ventures, resulting in over $30 billion in aggregate value. The current Flagship ecosystem comprises 37 transformative companies, including: Moderna Therapeutics (NASDAQ: MRNA), Rubius Therapeutics (NASDAQ: RUBY), Indigo Agriculture, and Sana Biotechnology.

Position Summary:

We are seeking a Scientist, Health Data Science 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.

Key Responsibilities:

• 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 detect with reasonably high success individuals before major changes in their health and forecast their time to detectable transition
• Develop methodologies to estimate rate-of-change for individuals between stages in health
• Ideate on how to align clinically relevant inflection points identified in electronic health records, clinical trials, or other sources with time-series biological data.
• 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
• Monitor and evaluate new and emerging technologies and models and identify opportunities for collaboration within Flagship Pioneering companies, academia, and third parties

Basic Requirements:

• PhD or equivalent level of experience in applying machine learning/artificial intelligence to large-scale clinical data. PhD may be in Biomedical Informatics, Bioinformatics, Machine Learning, Statistics, Computer Science, Data Science, Mathematics, or similar technical fields
• Demonstrated experience applying deep learning models to electronic health record or administrative claims data
• Fluency in Python and experience with R and SQL
• Familiarity with common machine-learning/deep-learning packages such as scikit-learn, TensorFlow and PyTorch
• Familiarity with AWS, GCP, or similar cloud-computing services
• Ability to thrive in an entrepreneurial and multidisciplinary environment

Preferred Requirements:

• Experience in one or more of the following areas/topics: regression models, regularization, time-series models, survival analysis, recurrent neural networks, graph neural networks, LST