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About

Charlie Wilson joined Paladin Capital Group in 2026 as the firm’s first Director of AI and Data Analytics, leading the build-out of AI and analytics tools that support investment research, portfolio monitoring, and internal operations.

Charlie’s recent work has focused on applied evaluation of large language models for finance-sector workflows, in partnership with a leading AI model developer. He designed detailed sector-specific evaluation rubrics to identify and characterize model failure modes, working in collaboration with the developer’s data science team. He was promoted to lead reviewer, calibrating evaluation standards across a team of writers grading model outputs. Late in the project, the scope expanded into early testing of agentic workflows.

Previously, Charlie spent thirteen years at Thornburg Investment Management, a Santa Fe-based global asset manager with roughly $50 billion in AUM, most recently as Managing Director and Portfolio Manager for the firm’s emerging markets and international equity strategies. From 2019 to 2021 he served as Thornburg’s first Director of Research, where he built the shared research platform, analytical frameworks, and governance standards that continue to support the firm’s equity research team. Before Thornburg, Charlie was a Partner and Senior Equity Analyst at Marsico Capital Management, a Denver-based high-conviction public equity manager, with coverage spanning global energy, materials, payments, technology, and healthcare.

Charlie’s technical foundation is in the earth sciences. He completed postdoctoral research at Stanford as a George A. Thompson Postdoctoral Research Scholar and at Columbia’s Lamont-Doherty Earth Observatory, with peer-reviewed publications on seismic imaging of the crust and upper mantle. He holds a Ph.D. in Geophysics from the University of Colorado Boulder and a B.S. in Geology from the University of Arizona. More recently he completed applied data science training at MIT and is finishing an M.S. in Analytics at Georgia Tech in the Computational Data Analytics track, with a focus on machine learning, natural language processing, and applied generative AI.