Data Analyst - Predictive Modeling Contractor

Position Overview: Role Title: Predictive Modeling Contractor (RDE 9 / DAISAE) Target Workstream: RDE 9 / RDE 9B2 (DAISAE Platform & Predictive Modeling Subteams)

Position Summary: 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. Key ResponsibilitiesAI 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 (Roche 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. Qualifications & Experience

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.