Pioneering Intelligence | Cambridge, MA Principal Scientist, Translational Modeling & Decision [...]

Principal Scientist, Translational Modeling & Decision Science Cambridge, MA USA About the Role Pioneering Intelligence (PI) builds AI systems that accelerate translational decision making. This 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 providing insights that improve the AI platform. The successful candidate will reduce scientific uncertainty behind Flagship’s drug development and investment decisions through mechanistic modeling and quantitative analysis. They will work across the portfolio, addressing diligence questions, milestone decisions, and competitive assessments, matching analytical rigor to the consequence of each decision, and flagging when findings from one program are 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 that stakeholders can carry into their own decisions. The role also identifies opportunities to close comprehension and trust gaps associated with complex work products. Responsibilities Translational Prediction & Decision Support Produce quantitative opinions across decision types and scenarios (due diligence, milestone & program decisions, competitive and what‑if analyses), decomposing claims into testable components, evaluating against evidence, and delivering 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 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 what good predictions look like, curate ground truth from real engagements, and evaluate output quality Prototype novel modeling approaches to prove feasibility and define acceptance criteria before handoff Review autonomous outputs for scientific correctness and feed failure cases back as regression tests and custom benchmarks/evaluations 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 Communicate translational results in Flagship‑internal forums and, where appropriate, external ones Deliver structured retrospectives on engagements (scientific outcome, decision informed, value created, collaborator feedback) Qualifications Required 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 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 Preferred 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 credibility frameworks (ICH M15, FDA MIDD) and their application to internal decision‑making Experience in venture‑backed or platform‑company environments where analysis supports investment decisions Equal Opportunity Employer We are an equal opportunity employer. All qualified applicants will be considered for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other characteristic protected by law. We recognize that great candidates often bring unique strengths without fulfilling every qualification. If you have some of the experience listed above but not all, please apply anyway. We are dedicated to building diverse and inclusive teams and look forward to learning more about your background and interest in Flagship. #J-18808-Ljbffr Flagship Pioneering