Overview In this role you will accelerate translational decisions by building mechanistic, quantitative models across Flagship's therapeutic portfolio. You'll work across programs to translate complex biology into actionable recommendations, quantify uncertainty, and drive outcomes that influence investments and program directions. You will serve as a bridge between science, AI engineering, and decision-makers, ensuring transparent assumptions and credible analyses. This position offers a high-impact opportunity to advance AI-enabled drug development at scale within a mission-driven, innovative ecosystem.
Compensation / Benefits- healthcare coverage
- annual incentive program
- retirement benefits
- broad range of other benefits
Responsibilities- Produce quantitative opinions across diligence, milestone, and program decisions, decomposing claims into testable components and delivering defensible conclusions
- Deliver translational predictions with confidence bounds for milestone decisions such as target engagement, therapeutic window, and modality feasibility
- Quantify probability of pharmacological success by integrating uncertainty across compound, mechanism, and disease dimensions
- Translate scientific complexity into decision-ready recommendations for non-technical stakeholders
- Carry insights between programs, flagging when translational risk affects other programs
- Define context-of-use, including the question, decision, credibility standard, and cost of being wrong
- Translate field discoveries into product requirements for Applied AI and Engineering teams
- Prototype novel modeling approaches and establish acceptance criteria before handoff
- Review autonomous outputs for scientific correctness and feed failure cases back as regression tests and benchmarks
- Set quality standards on every engagement with rigorous evidence evaluation and transparent uncertainty quantification
- Communicate translational results in internal and, where appropriate, external forums
- Deliver retrospectives on engagements detailing scientific outcomes, decisions informed, and value created
Key requirements- PhD in life sciences with quantitative experience, or a quantitative life sciences discipline (pharmacometrics, systems pharmacology, computational biology, biomedical engineering) with strong knowledge of physiology, molecular biology, and immunology
- Proven expertise building mechanistic models for drug development decisions across multiple therapeutic areas
- Ability to translate scientific complexity into recommendations for non-technical decision-makers, with excellent communication 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
- excellent communication skills
- composure under pressure
- cross-functional collaboration
- Python scientific computing (scipy, numpy, pandas)
- numerical ODE integration
- mechanistic modeling for drug development
