Description

Responsibilities

• Develop and prototype predictive models and machine learning solutions for drug safety applications
• Design and implement agentic AI systems, large language model (LLM) tools, and MCP (Model Context Protocol) tools
• Process, analyze, and model biomedical datasets including omics data, clinical trial data, real-world data (RWD), and in vitro/in vivo experimental data
• Collaborate with cross-functional teams using Agile methodologies
• Support rapid prototyping cycles and iterative development of AI-driven safety risk prediction capabilities

Qualifications

• Required
  • Master's degree or PhD in Data Science, Computer Science, Computational Biology, Bioinformatics, Biostatistics, Epidemiology, or a related quantitative field
  • 3+ years of hands-on experience developing predictive models, machine learning solutions, and AI prototyping
  • 3+ years of experience developing agentic AI systems, LLM tools, and MCP tools
  • 3+ years of proficiency in handling biomedical datasets (omics, clinical trials, RWD, or in vitro/in vivo data)
  • 2+ years of experience working with Agile methodologies
• Preferred
  • 5+ years of background in pharmacovigilance, drug safety, toxicology, or quantitative risk management

Key Skills

Education

Master's degree