Formation Bio is looking to solve the problem of high cost and time of clinical trials in drug development by leveraging AI and technology platforms to accelerate all aspects of drug development and clinical trials.
Requirements
PhD in computational sciences or life sciences
Deep expertise in AI/ML for predicting biological/clinical outcomes with validated examples
Demonstrated success in partnering with engineering/product teams to productize data science capabilities
Track record of building models that influenced drug acquisition, licensing, or portfolio decisions
Experience transitioning AI/ML models from research to production in regulated environments
Strong experience with LLMs, Graph Neural Networks, and multi-modal data integration (-omics, RWD, literature)
Responsibilities
Lead the Platform Science arm of Data Science, building predictive models that drive drug acquisition and portfolio optimization decisions
Partner with engineering and product teams to transition proof-of-concept models into scalable, production-ready systems
Define technical execution strategy for clinical outcome prediction, indication expansion, and asset prioritization
Drive the Probability of Technical Success (PoTS) program and predictive modeling efforts using multi-modal data (literature, -omics, RWD)
Collaborate with executive leadership, business development, and clinical teams to translate predictions into acquisition strategies
Foster a culture of rapid execution while maintaining rigorous validation standards
Other
7+ years of post-PhD industry experience in life sciences (biotech, pharma, or consulting) with at least 3+ years in people management
Exceptional communication skills with ability to influence senior executives and deliver on aggressive timelines
Please only apply if you reside in the New York City and Boston metro areas, with additional growth in the Research Triangle (NC) and San Francisco Bay Area, or are willing to relocate
You will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status
