Predictive Modeling Contractor (RDE 9 / 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. Responsibilities: 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.

Our Client, a Biotech company, is looking for a Predictive Modeling Contractor (RDE 9 / DAISAE) for their South San Francisco, CA location.
 
Responsibilities:

• 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 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.

 
Requirements:

• 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.

 
Why Should You Apply?

• Health Benefits
• Referral Program
• Excellent growth and advancement opportunities

Why Should You Apply?

• Health Benefits
• Referral Program