Principal Scientist, Translational Modeling & Decision Science

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. 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. 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 identification 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 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 develo...