Description
FL113 is a fast-moving AI diagnostics company with a bold ambition: to define the future of pre-emptive and proactive care. We are building products that help uncover disease before it becomes visible, equip patients and care teams with earlier insight, and enable meaningful intervention before disease advances and causes irreversible harm.
The Vice President of AI Research and Real-World Evidence owns FL113's scientific agenda, model methodology and performance, clinical validation, and evidence generation. You will determine which scientific questions to pursue, how to assess model performance and clinical utility, and what evidence is required to support product readiness, customer adoption, and regulatory strategy.
Key Responsibilities:
- Set the Scientific Agenda: Own and continuously evolve a multi-year research roadmap spanning predictive modeling, causal inference, and longitudinal analysis, informed by scientific, clinical, product, and market evidence.
- Lead Model Development and Validation: Define the scope and design of research programs, including indication selection, data strategy, modeling approach, endpoints, and validation criteria.
- Establish Product Readiness: Define scientific acceptance criteria and productization requirements with Product and Engineering.
- Build Clinical Evidence and Scientific Credibility: Design and oversee retrospective and prospective clinical studies with health-system partners.
- Build and Lead the Research Organization: Recruit, lead, and develop a multidisciplinary team spanning machine learning, clinical research, real-world evidence, biostatistics, and health economics.
What We Look For:
- PhD or master's degree in biostatistics, computational biology, epidemiology, mathematics, computer science, or another relevant quantitative discipline, combined with deep healthcare or life-sciences experience.
- At least 12 years of experience across AI research, health data science, clinical evidence generation, or product development, including success in early-stage or zero-to-one environments.
- Deep hands-on expertise in machine learning for structured and unstructured health data, including causal inference, deep learning, survival analysis, and longitudinal modeling.
The salary range for this role is $263,000 - $346,500.