Predictive Modeling Contractor

Location:

14 months

Duration:
South San Francisco, CA 94080 (open to remote as well)

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 (Client's 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.

• Education: MSc or PhD in Data Science, Computer Science, Computational Biology, Bioinformatics, Biostatistics, Epidemiology, or a related quantitative field.
• Technical Expertise:
  • 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.
• Domain Knowledge:
  • Background in pharmacovigilance, drug safety, toxicology, or quantitative risk management is highly desirable.
• Soft Skills:
  • "AI native" approach to problem-solving with adaptability to fast-moving frameworks.
  • Proactive attitude and strong communication skills suitable for global matrix team settings.