About this role
About Formation Bio
Formation Bio is a tech and AI driven pharma company differentiated by radically more efficient drug development.
Advancements in AI and drug discovery are creating more candidate drugs than the industry can progress because of the high cost and time of clinical trials. Recognizing that this development bottleneck may ultimately limit the number of new medicines that can reach patients, Formation Bio, founded in 2016 as TrialSpark Inc., has built technology platforms, processes, and capabilities to accelerate all aspects of drug development and clinical trials. Formation Bio partners, acquires, or in-licenses drugs from pharma companies, research organizations, and biotechs to develop programs past clinical proof of concept and beyond, ultimately helping to bring new medicines to patients. The company is backed by investors across pharma and tech, including a16z, Sequoia, Sanofi, Thrive Capital, John Doerr, Spark Capital, SV Angel Growth, and others.
You can read more at the following links:
- Our Vision for AI in Pharma
- Our Current Drug Portfolio
- Our Technology & Platform
At Formation Bio, our values are the driving force behind our mission to revolutionize the pharma industry. Every team and individual at the company shares these same values, and every team and individual plays a key part in our mission to bring new treatments to patients faster and more efficiently.
About the Position
This role sits on Formation's Asset Acquisition & Diligence (AAD) team. In the course of evaluating and completing diligence on individual drug assets, our Data Science team runs sophisticated one-off analyses (e.g. a novel algorithm run against a specific gene or indication, a causal model built for a specific therapeutic hypothesis). Much of that work directly shapes a specific deal decision in the moment.
This role exists to take these and scale them into strategies and tools that can provide an organization level advantage. You will own the judgment call of which asset-evaluation and diligence analyses have generalization potential across the AAD team's sourcing and evaluation work, and what form that generalization should take: sometimes a landscape analysis (the same method run not on one gene, but on every gene, surfaced as a standing analysis that informs sourcing rather than a product), sometimes a fully engineered, scaled tool built in partnership with Data Science and Engineering. You are the connective tissue between one-off diligence work and durable diligence and sourcing capability.
This is a hybrid science and product role on AAD. You will set technical and product direction for a working team of data scientists and engineers who continue to report to their functional leads. You'll need enough technical depth to judge when a diligence method holds up at scale, and enough product judgment to know when “scale”