Vice President, AI Research and Real-World Evidence

Role: Vice President of AI Research and Real-World Evidence leading FL113’s scientific agenda, model validation, and evidence generation. Responsibilities: define and execute a multi-year research roadmap in predictive modeling, causal inference, and longitudinal analysis; oversee model development, validation, and performance assessment; establish product readiness criteria; design and manage clinical studies in partnership with health systems and biopharma clients; build and lead a multidisciplinary research team; ensure scientific rigor, model quality, and regulatory compliance. Requirements: PhD or master's in biostatistics, computational biology, epidemiology, or related field; 12+ years in AI research, health data science, or clinical evidence generation; hands-on expertise with machine learning in structured/unstructured health data, including causal inference, deep learning, survival analysis, and longitudinal models; experience translating models from research to production, clinical study design, and evidence development; proficiency with Python, PyTorch, scikit-learn; deep understanding of health data standards (FHIR, HL7, OMOP, SNOMED); familiarity with healthcare privacy/security regulations. Preferred: experience in early-stage AI-enabled health tech companies, building high-performing teams, and engaging with clinical and regulatory stakeholders. High-Value: focus on medical diagnostics, predictive analytics, real-world evidence, clinical validation, and AI model deployment in healthcare. Work Setup: not explicitly stated, likely collaborative with cross-functional teams, remote/hybrid environment possible.