Principal Scientist, Translational Modeling & Decision Science

About the role

Flagship Pioneering, Inc. is seeking a highly skilled Principal Scientist to join its Pioneering Intelligence team. This role is central to leveraging artificial intelligence and advanced modeling techniques to enhance decision-making processes in translational science and drug development. The successful candidate will be instrumental in reducing uncertainty and improving the quality of strategic decisions related to therapeutic programs. They will work closely with cross-functional teams to develop quantitative insights, generate predictive models, and communicate complex scientific findings in a clear and actionable manner. This position offers an to contribute to innovative projects at the forefront of biotech and AI integration, supporting Flagship's mission to transform scientific discoveries into impactful medicines.

Key facts

What you'll do

• Generate detailed quantitative insights across various decision-making scenarios, including due diligence assessments, milestone evaluations, and portfolio prioritization, by systematically breaking down claims into testable components and assessing them against available scientific and clinical evidence.
• Provide translational predictions with clearly defined confidence levels for critical decisions such as target engagement, therapeutic feasibility, and clinical trial success probabilities.
• Assess the likelihood of pharmacological success by integrating uncertainties associated with compounds, mechanisms of action, disease models, and patient populations, thereby supporting risk mitigation strategies.
• Simplify complex scientific data, including preclinical and clinical findings, into clear, concise, and actionable recommendations for stakeholders, enabling confident decision-making at various stages of drug development.
• Communicate insights effectively across different programs and teams, emphasizing relevant translational risks and opportunities, and ensuring alignment with strategic goals.
• Identify the most impactful areas where predictive science and modeling can add value, especially at key development milestones, to optimize resource allocation and accelerate progress.
• Establish the context for each engagement by clarifying the specific questions, decision points, and standards of evidence or credibility involved, ensuring that modeling approaches are appropriately tailored.
• Adjust the level of analytical rigor based on the importance of each decision, aligning with regulatory standards when necessary, and ensuring scientific validity and reproducibility.
• Translate discoveries from the field, such as biomarker data or mechanistic insights, into actionable product requirements and specifications for the Applied AI and Engineering teams.
• Define and uphold quality standards for scientific evaluations, including rigorous evidence assessment, validation of models, and reproducibility of results.
• Share translational outcomes and insights internally through presentations, reports, and collaborative discussions, and externally when appropriate, to foster transparency and knowledge sharing.
• Conduct structured reviews of ongoing engagements to evaluate scientific results, decision impacts, and the overall value generated, continuously refining approaches based on lessons learned.
• Collaborate with multidisciplinary teams, including biologists, clinicians, data scientists, and engineers, to integrate diverse expertise into modeling and decision frameworks.
• Stay current with advances in AI, modeling techniques, and regulatory guidelines relevant to translational science, incorporating best practices into daily work.
• Support the development of new methodologies and tools that enhance the predictive capabilities and decision-making processes within the organization.

Requirements

• PhD in a life sciences field such as pharmacology, biology, biomedical engineering, or related quantitative discipline, with extensive experience in applying quantitative methods to drug development.
• Proven track record in developing and applying mechanistic models for drug discovery and development across multiple therapeutic areas.
• Strong understanding of physiology, molecular biology, immunology, and pharmacology, with the ability to integrate these disciplines into modeling efforts.
• Demonstrated ability to communicate complex scientific concepts clearly and effectively to both technical and non-technical audiences, maintaining composure in high-pressure situations.
• Proficiency in Python, including libraries such as scipy, numpy, and pandas, along with experience in numerical ordinary differential equation (ODE) integration and scientific computing.
• Experience managing multiple concurrent programs and decision-making processes, prioritizing tasks effectively to meet organizational goals.
• Knowledge of regulatory standards and guidelines relevant to drug development and translational science.
• Ability to work independently and collaboratively within a multidisciplinary team, fostering a culture of scientific rigor and innovation.
• Strong analytical skills, with a focus on reproducibility, validation, and quality assurance in modeling and data analysis.
• Experience in translating scientific discoveries into practical product or project requirements, facilitating integration with engineering and applied AI teams.

Nice to have

• Experience with advanced artificial intelligence tools, including large language models, and their application in drug discovery and development contexts.
• Broad knowledge of various drug modalities, such as small molecules, biologics, gene therapies, and emerging platform technologies.
• Familiarity with the latest regulatory guidelines and frameworks governing drug development, including those related to modeling and simulation.
• Experience working in a fast-paced, innovative biotech environment, contributing to strategic planning and decision-making.
• Knowledge of computational biology, systems pharmacology, or related fields that complement modeling efforts.

Skills & tools

• Python (scipy, numpy, pandas)
• Mechanistic modeling and simulation
• Quantitative data analysis and interpretation
• Effective scientific communication and presentation skills
• Cross-functional collaboration and teamwork
• Knowledge of AI and machine learning applications in biotech

Practical notes

• Visa sponsorship may be available for qualified candidates, supporting international applicants.
• The organization offers a competitive salary package, comprehensive health insurance, and opportunities for professional development and growth.
• The role involves on-site work at the Cambridge, MA office; remote work options are .
• Applications will be accepted until the position is filled, so early submission is encouraged.
• Candidates should be prepared to demonstrate their experience with modeling, decision science, and translating scientific insights into strategic actions.
• The organization values diversity and inclusion, welcoming applicants from varied backgrounds to contribute to its innovative mission.