Flagship Pioneering is an innovation enterprise that conceives, creates, resources, and grows 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 $19+ billion in aggregate value, 500+ issued patents, and more than 50 clinical trials for novel therapeutic agents. These companies include Seres Therapeutics (NASDAQ:MCRB), Moderna, Syros Pharmaceuticals (SYRS), Rubius Therapeutics (RUBY), Axcella Health, Evelo Biosciences (EVLO), and Indigo Agriculture.
This is a part-time position. We are seeking a highly motivated computer scientist with a strong background in Machine Learning to be a thought partner in a project that aims to fundamentally change the way we innovate in science and technology. The ideal candidate will have a track record of taking ideas to real-world applications. In this position, the person will apply his/her creativity and problem-solving skills to challenges and opportunities in life-science innovation. Specifically, he/she will spend 10-20 hours/week working alongside a venture origination team in developing ML models to augment or replace scientific innovation. Experience in Graph Neural Networks is a strong plus.
- Quickly iterate on pilot proof-of-concept experiments to test new ideas
- Thought partner during the ideation process
- Write and present results to the team
- Monitor and evaluate new and emerging technologies
- PhD, master’s or bachelor’s degree in a quantitative discipline (e.g. Machine Learning, Statistics, Computer Science, Mathematics, Computational Biology or related field)
- Experience with ML tools and packages (e.g. Deep Graph Library, PyTorch, Snorkel, GraphSAGE, etc.)
- Strong coding and software engineering skills in at least a mainstream programming language (e.g. Python, Java, R, C/C++, etc.)
- In-depth knowledge of ML algorithms
- Motivated, curious, execution focused, team oriented, with an ability to thrive in an entrepreneurial and multidisciplinary environment