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 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 

ABOUT FLAGSHIP PIONEERING:

Flagship Pioneering invents and builds platform companies, each with the potential for multiple products that transform human health, sustainability and beyond. Since its launch in 2000, Flagship has originated more than 100 companies. Many of these companies have addressed humanity’s most urgent challenges: vaccinating billions of people against COVID-19, curing intractable diseases, improving human health, preempting illness, and feeding the world by improving the resiliency and sustainability of agriculture.  

Flagship has been recognized twice on FORTUNE’s “Change the World” list, an annual ranking of companies that have made a positive social and environmental impact through activities that are part of their core business strategies and has been twice named to Fast Company’s annual list of the World’s Most Innovative Companies. Learn more about Flagship at www.flagshippioneering.com.

At Flagship, we accept impossible missions to enable bigger leaps. Our core values guide us through uncertainty and toward lasting impact.

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.

Recruitment & Staffing Agencies: Flagship Pioneering and its affiliated Flagship Lab companies (collectively, “FSP”) do not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to FSP or its employees is strictly prohibited unless contacted directly by Flagship Pioneering’s internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of FSP, and FSP will not owe any referral or other fees with respect thereto.

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The salary range for this role is $179,000 - $236,000. Compensation for the role will depend on a number of factors, including a candidate’s qualifications, skills, competencies, and experience. Pioneering Intelligence currently offers healthcare coverage, annual incentive program, retirement benefits and a broad range of other benefits. Compensation and benefits information is based on Pioneering Intelligence's good faith estimate as of the date of publication and may be modified in the future.