Predictive Modeling Contractor

Role

Title:

Predictive Modeling Contractor (RDE 9 / DAISAE)

Target Workstream: RDE 9 / RDE 9B2 (DAISAE Platform & Predictive Modeling Subteams)

Preferred

Location:

South San Francisco (SSF), CA;open to Basel, Switzerland or remote depending on candidate fit

Duration:
November 1, 2026 December 31, 2027 (approx. 14 months)

Approved Budget: CHF
120, 000 for remainder of 2026 / CHF
250, 000 for 2027

We are seeking a contractor with a background combining AI rapid prototyping and Predictive Modeling to support the DAISAE platform and predictive analytics subteams within the RDE 9 program. This role focuses on co-developing shared data and technical foundations (e.G. Ontologies, Knowledge Graphs), prototyping agentic AI capabilities, and advancing predictive models for drug safety risk prediction across the product lifecycle.

• AI Prototyping & MCP Tool Development:
  Rapidly explore, evaluate, and prototype emerging AI tools and agentic capabilities, specifically developing MCP tools and AI agent integrations for DAISAE.
• Predictive Modeling:
  Build and refine predictive models using multi-modal datasets (including in vitro, in vivo, omics, clinical trial data, real-world data (RWD), and spontaneous safety reports) to enable risk prediction from early molecule-level evaluation to late-stage and post-marketing patient safety.
• Architecture & Platform Design:
  Translate feasibility findings and prototype evaluations into concrete architectural design and platform requirements for shared data and technical foundations.
• Co-Development & Governance
  
  Collaboration:
  
  Work in an agile way to prototype reusable technical capabilities before transitioning components to RDT (
  *** Digital Tech) for formal validation, compliance, and production rollout.
• Cross-Functional Alignment:
  Collaborate with data scientists, safety scientists, and RDT technical partners to ensure technical execution aligns with business priorities.

MSc or PhD in Data Science, Computer Science, Computational Biology, Bioinformatics, Biostatistics, Epidemiology, or a related quantitative field.

Strong expertise in predictive modeling, machine learning, and AI prototyping. Hands-on experience developing agentic AI systems, LLM tools, and MCP tools. Proficiency in handling biomedical datasets (omics, clinical trials, RWD, or in vitro/in vivo data). Agile prototyping mindset with software engineering skills tailored for data science.

Background in pharmacovigilance, drug safety, toxicology, or quantitative risk management is highly desirable.

"AI native" approach to problem-solving with adaptability to fast-moving frameworks. Proactive attitude and strong communication skills suitable for global matrix team settings.