Data Analyst - Predictive Modeling

Job DescriptionJob DescriptionRole : Data Analyst - Predictive ModelingLocation : South San Francisco, CA- HybridDuration : 12+ Month ContractJob Summary: Predictive Modeling Contractor (RDE 9 / DAISAE) Target Workstream: RDE 9 / RDE 9B2 (DAISAE Platform & Predictive Modelling Subteams) Position Summary We are seeking a contractor with a background combining AI rapid prototyping and Predictive Modelling 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 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 (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.