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

Flagship Pioneering; Cambridge (MA); On-site; USD 180,000 - 280,000; Full time

Job summary

Flagship Pioneering's Pioneering Intelligence team in Cambridge, MA seeks a Principal Scientist to lead translational modeling and decision science. You will build mechanistic models to reduce uncertainty in drug development decisions and provide quantitative insights across programs.

You will shape product requirements for AI-enabled translational tools, define context of use, and communicate results to stakeholders with defensible assumptions and transparent uncertainty.

Qualifications

• PhD in a life sciences discipline with quantitative experience, or a quantitative life sciences PhD with strong physiology, molecular biology, and immunology knowledge.
• Demonstrated expertise building mechanistic models for drug development decisions across multiple therapeutic areas.
• Ability to translate scientific complexity into actionable recommendations for non-technical decision-makers with strong communication and composure.
• Proficiency in Python scientific computing (scipy, numpy, pandas) and numerical ODE integration.
• Experience operating across multiple concurrent programs and decision types.
• Experience applying frontier AI and LLM tools to drug development decisions.
• Translational breadth across drug modalities (small molecule, biologic, gene therapy, etc.).
• Familiarity with regulatory credibility frameworks (ICH M15, FDA MIDD) and their internal decision-making applications.
• Experience in venture-backed or platform-company environments where analysis supports investment decisions.

Responsibilities

• Translational Prediction & Decision Support: produce quantitative opinions across diligence, milestone, and program decisions, decomposing claims into testable components.
• Deliver translational predictions with 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.
• Carry insight between programs and flag translational risks relevant to others.
• Prioritize where predictive science creates the most decision value at each milestone.
• Define context-of-use and credibility standards for each engagement.
• Match analytical rigor to decision consequence using regulatory concepts.

Skills

PhD in life sciences (quantitative)

Mechanistic modeling

Scientific communication

Python scientific computing

Numerical ODE integration

Program management across programs

Education

PhD in life sciences or quantitative life sciences

Tools

Python (scipy, numpy, pandas)

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.