Flagshippioneeringinc
Principal Scientist, Translational Modeling & Decision Science
Cambridge, MA USA · Principal
Sponsorship not specifiedDetected 7 days ago
PythonMachine LearningPandasNumPyLLMsPharmacologyFDA RegulatoryBioinformaticsCRISPRMolecular BiologyResearchCommunication
About the role
- The successfu l candidate will r educe the scientific uncertainty behind Flagship's drug development and investment decisions through mechanistic modeling and quantitative analysis.
Responsibilities
- Translational Prediction & Decision Support
- Produce quantitative opinions across decision types and scenarios (due diligence, milestone and program decisions, competitive and what-if analyses): decompose claims into testable components, evaluate against evidence, and deliver conclusions on the decision's timeline
- Deliver translational predictions with stated confidence and boundary conditions for milestone decisions (target engagement, therapeutic window, modality feasibility, competitive differentiation)
- Translate scientific complexity into recommendations stakeholders can carry and defend in their own decisions, not just a number to take on trust
- Prioritize where predictive science creates the most decision value at each development milestone
Requirements
- PhD in a life sciences discipline with quantitative experience, or in a quantitative life sciences discipline (pharmacometrics, systems pharmacology, computational biology, biomedical engineering, or equivalent) with strong knowledge of physiology, molecular biology, and immunology
- Demonstrated expertise building mechanistic models for drug development decisions across multiple therapeutic areas
Nice to have
- Communicate translational results in Flagship-internal forums and, where appropriate, external ones
- Ability to translate scientific complexity into recommendations for non-technical decision-makers with excellent communication skills and composure under pressure
- Proficiency in Python scientific computing (scipy, numpy, pandas) and numerical ODE integration
- Experience operating across multiple concurrent programs and decision types
- Experience applying frontier AI and LLM tools to drug development decisions
- Translational breadth across drug modalities (small molecule, biologic, gene therapy, novel platforms)
- Familiarity with regulatory
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