Director, Health Data Science
Flagship Pioneering · Cambridge, MA USA
About this role
## Director, Health Data Science **Who We Are** FL113 is a fast-moving AI diagnostics company building products that uncover disease before it becomes visible. We equip patients and care teams with earlier insight to enable meaningful intervention. Our team brings together ML researchers, serial entrepreneurs, and clinical experts united by a commitment to make proactive care a reality. FL113 is part of the Flagship Pioneering ecosystem, recognized twice on FORTUNE's "Change the World" list and twice on Fast Company's list of the World's Most Innovative Companies. **About the Role** The Director of Health Data Science owns FL113's scientific agenda, model methodology and performance, clinical validation, and evidence generation. You will determine which scientific questions to pursue, assess model performance and clinical utility, and define evidence required for product readiness, customer adoption, and regulatory strategy. You'll work closely with leaders across Engineering, Product, and GTM, as well as customers and health-system partners, to shape product development while retaining accountability for scientific rigor and real-world evidence. **Key Responsibilities** **Set the Scientific Agenda** • Own and evolve a multi-year research roadmap spanning predictive modeling, causal inference, and longitudinal analysis • Translate roadmap into practical milestones, decision gates, and resource requirements • Connect model performance to clinical value, customer ROI, and development time • Define fit-for-purpose standards for data quality, model validation, and responsible AI use • Apply AI-enabled tools to accelerate research and team productivity **Lead Model Development and Validation** • Define scope and design of research programs including indication selection, data strategy, and validation criteria • Oversee model development across structured and unstructured health data • Own model methodology, evaluation plans, and evidence for performance and clinical utility • Mentor research scientists and uphold scientific quality • Define research dataset requirements and data-quality criteria with Engineering **Establish Product Readiness** • Define scientific acceptance criteria and productization requirements • Develop model-readiness and validation plans for production and customer pilots • Provide scientific requirements for interoperability, data architecture, and monitoring • Confirm production implementations preserve validated model behavior • Inform feature requirements and launch readiness **Build Clinical Evidence and Scientific Credibility** • Design and oversee retrospective and prospective clinical studies • Establish real-world evidence strategy for evaluating product performance and clinical utility • Shape scientific design for pilots with biopharma, ACOs, and health systems • Partner with clinical and regulatory experts on evidence plans • Communicate results, limitations, and risks to stakeholders • Build credibility through engagement with key opinion leaders and publications **Build and Lead the Research Organization** • Recruit and develop a multidisciplinary team spanning ML, clinical research, biostatistics, and health economics
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