Summary
Jimmy Hickey is a Machine Learning Engineer with a decade of experience applying statistical and deep learning methods across healthcare, national defense, and research environments. He holds advanced training in statistics (PhD) and has published and deployed novel transfer learning and time-to-event prediction methods that emphasize interpretability and uncertainty quantification. At Sandia he optimized statistical computing and built RNN-based signal processing for defense applications, and at Optum he continues to translate research-grade models into production. He co-founded a community analytics nonprofit, demonstrating a habit of turning data into actionable, human-centered insights, and he has a strong background building high-performance bioinformatics and embedded systems earlier in his career. Notably, his work consistently bridges rigorous theory and practical implementation across Python, R, Julia, and low-level C/C++ environments.
10 years of coding experience
10 years of employment as a software developer
Doctor of Philosophy - PhD Statistics, Doctor of Philosophy - PhD Statistics at North Carolina State University
Computer Science (BS) Physics (BS), Computer Science (BS) Physics (BS) at Winona State University
English