Vice President, AI Research and Real-World Evidence
About this role
Directing the scientific agenda and real-world evidence strategy, this executive leads model methodology, clinical validation, and study design while partnering closely with engineering and product teams. The individual in this role owns research roadmaps, defines scientific acceptance criteria, and oversees the development of predictive, causal, and longitudinal models using complex healthcare data. Operating within an early-stage venture creation ecosystem, this leader balances rigorous scientific standards with rapid execution to build diagnostic products that uncover disease before it becomes visible.
Accountable for establishing product readiness and building scientific credibility, the selected candidate recruits and mentors a multidisciplinary team spanning machine learning, biostatistics, and clinical research. Success in this position requires translating ambiguous clinical challenges into practical development milestones, designing retrospective and prospective studies with health-system partners, and communicating technical risk effectively to internal and external stakeholders.
What you will do
- Formulate and evolve a multi-year research roadmap encompassing predictive modeling, causal inference, and longitudinal analysis based on clinical and market evidence.
- Define fit-for-purpose standards for data quality, model validation, study design, and reproducibility.
- Oversee model development across structured and unstructured health data in partnership with engineering and product teams.
- Design and direct retrospective and prospective clinical studies alongside health-system partners and early customers.
- Establish the real-world evidence strategy to evaluate product performance, clinical utility, workflow impact, and economic benefit.
- Recruit, lead, and develop a multidisciplinary team of machine learning researchers, biostatisticians, and clinical experts.
What you need
- Hold a PhD or master’s degree in a quantitative discipline such as biostatistics, computational biology, epidemiology, mathematics, or computer science, accompanied by deep healthcare or life-sciences experience.
- Bring a minimum of 12 years of experience spanning AI research, health data science, clinical evidence generation, or product development, including success in early-stage environments.
- Demonstrate deep hands-on expertise in machine learning for healthcare data, covering causal inference, deep learning, survival analysis, and longitudinal modeling.
- Show proven experience taking clinical AI models from research through real-world validation and into production.
- Possess experience designing, executing, and interpreting retrospective and prospective clinical studies.
- Maintain fluency with modern research tooling including Python, PyTorch, and scikit-learn.
- Display strong working knowledge of diverse health data sources and healthcare data standards such as FHIR, HL7, and OMOP.
Skills and tech stack
Questions candidates ask
What is the salary range for this position?
The salary for this role is between $263,000 and $346,500.
Where is the job located?
This position is located in Cambridge, MA USA.
What type of employment is this?
This is a full-time role.
What kind of company is FL113?
FL113 is a fast-moving AI diagnostics company building products that uncover disease before it becomes visible and part of the Flagship Pioneering ecosystem.
The application is on the official Flagship Pioneering, Inc. careers page.
Apply on company site