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

Job description

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.

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Worksite address

cambridge, MA, 02140, US

Who can apply

Review the original listing for work authorization, qualifications and employer requirements.