Dan is an Operating Advisor at JMI, where he helps portfolio teams move from prototype to production by building chatbots, designing agentic workflows, and leveraging classic machine learning so structured and unstructured data translate into actionable insights and improved efficiency. At Sovrn Holdings, he developed an NLP pipeline to quantify customer sentiment and highlight key pain points, alongside an ML process for revenue forecasting. He has also supported Canto by automating financial reporting for revenue-growth components, and served as a statistics tutor at the University of Denver. He holds an advanced degree focused on statistics, machine learning, deep learning, natural language processing, and AI, culminating in a capstone project that enhanced a foundational LLM through a tuned tensor layer.
