Senior Director MMAI & Outcome Prediction - AI for Precision Health

Experteer Overview

Your work enables earlier detection, refined stratification, and improved decision-making across Oncology and BioPharma, with an emphasis on adoption, governance, and real-world impact. This is a cross-functional leadership position focused on robust, regulatory-ready solutions that accelerate precision medicine.

• Act as the enterprise scientific authority for multimodal AI in Oncology and BioPharma, defining a scientific agenda and advancing multimodal methodologies.
• Lead AI-enabled diagnostic frameworks and biomarker discovery to support early diagnosis and disease taxonomy.
• Apply multimodal AI to transform clinical development, including trial enrichment and endpoint optimization.
• Reinforce foundation models with clinical and real-world data, creating reusable representations for scalable modelling.
• Integrate clinical trials and real-world evidence into continuous learning systems for ongoing model reinforcement.
• Establish enterprise standards for validation, regulatory-grade evidence, and cross-site robustness of AI models.
• Bridge R&D, diagnostics, and care transformation to deploy scalable predictive tools in real-world practice.
• Develop strategic external partnerships and evaluate external data and algorithms for robustness and regulatory readiness.
• Foster cross-functional collaboration across research, diagnostics, and commercial teams to align strategies.
• Elevate organizational capability in AI-driven precision medicine and mentor scientific/digital teams.

• Advanced degree (Master's or PhD) in a relevant field
• +5 years of experience leading or contributing to AI-enabled medical/biological projects
• Expertise in multimodal AI for Oncology and BioPharma with impact on outcome prediction or computational diagnostics
• Proficiency in multimodal representation learning, survival modelling, causal inference, and external control arms
• Experience with regulatory-grade validation, reproducibility, and model lifecycle monitoring
• Knowledge of AI governance, data privacy regulations, and healthcare regulatory pathways
• Experience with large-scale heterogeneous healthcare datasets (EHR, claims, imaging, genomics, trial databases)

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