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About Pioneering Intelligence Pioneering Intelligence builds on Flagship Pioneering’s legacy of founding cutting-edge science and computational ventures, harnessing recent advances in AI, machine learning, and data to accelerate fundamental research and create a portfolio of AI-first companies. As part of Flagship’s integrated model of science, entrepreneurship, and capital, it transforms breakthrough ideas into world-changing companies, elevating the AI advances happening across the ecosystem in human health, sustainability, and beyond. About the Role   Pioneering Intelligence  ( PI) is building  AI  systems  that  accelerate translational  decision making .  Th is forward-deployed role  bridges  the  gap  from  capability to impact   by  leveraging PI technologies to  tak e  on  tr anslation al challenges   across Flagship’s  therapeutic  portfolio ,  delivering  value  through  programs and  business decisions   while  returning insights that improve   the  AI  platform.     The successfu l candidate  will r educe  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, tak ing  on diligence questions, milestone decisions, and competitive assessments as they arise, matc hing  analytical rigor to the consequence of each decision, and flag ging  when one program’s finding is relevant to another. They will also serve as an expert test user  for the Applied AI and Engineering teams, feeding field discoveries back as product requirements.   The role owns the credibility of its analyses: transparent uncertainty, defensible assumptions, and results a stakeholder can carry into their own decision . The  role also owns the  identifi cation of  opportunities to close comprehension and trust gaps associated with complex agentic work products.   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)   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, not just a number to take on trust   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   Match analytical rigor to decision consequence using regulatory credibility concepts (ICH M15, FDA MIDD) adapted for internal decisions   Feedback Loop into the Agentic Platform   Translate field discoveries into product requirements for the Applied AI and Engineering teams   Provide domain-expert signal: define