Description
Pioneering Intelligence (PI) is building AI systems that accelerate translational decision making. This forward-deployed role bridges the gap from capability to impact by leveraging PI technologies to take on translational challenges across Flagship’s therapeutic portfolio, delivering value through programs and business decisions while returning insights that improve the AI platform.
The successful candidate will reduce the scientific uncertainty behind Flagship’s drug development and investment decisions through mechanistic modeling and quantitative analysis. The individual will work across the portfolio rather than within a single program, taking on diligence questions, milestone decisions, and competitive assessments as they arise, matching analytical rigor to the consequence of each decision, and flagging when one program’s finding is relevant to another.
Responsibilities:
- Translational Prediction & Decision Support: Produce quantitative opinions across decision types and scenarios, 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.
- Quantify probability of pharmacological success by integrating uncertainty across compound, mechanism, and disease dimensions.
- Translate scientific complexity into recommendations stakeholders can carry and defend in their own decisions.
- Cross-Portfolio Pattern Recognition: Carry insight between programs, flagging when one company’s translational risk is relevant to another.
- Prioritize where predictive science creates the most decision value at each development milestone.
- Define context-of-use for each engagement: what question, what decision, what credibility standard, what cost of being wrong.
- Feedback Loop into the Agentic Platform: Translate field discoveries into product requirements for the Applied AI and Engineering teams.
- Provide domain-expert signal: define what good predictions look like, curate ground truth from real engagements, and evaluate output quality.
- Scientific Standards & Execution: Personally execute problems that require expert judgment: novel biology, new modalities, or high-consequence analyses.
- Set quality standards on every engagement: rigorous evidence evaluation, transparent uncertainty quantification, reproducible methodology.
Qualifications:
- PhD in a life sciences discipline with quantitative experience, or in a quantitative life sciences discipline with 3 to 7 years of industry experience.
- Demonstrated 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 skills and composure under pressure.
- Proficiency in Python scientific computing (scipy, numpy, pandas) and numerical ODE integration.