Daniel Marley is a Senior Data Scientist based in Raleigh, NC, with a decade of experience translating cutting-edge machine learning research into production across healthcare, agriculture, and sports analytics. He blends a PhD in particle physics with hands-on expertise in computer vision, graph neural networks, and FPGA-accelerated inference—skills honed at institutions from CERN experiments to industry teams at Intelinair and Stats Perform. Daniel has led end-to-end ML systems: building multi-agent player-tracking pipelines, custom TensorFlow libraries for graph-based event detection, and cloud-deployed models that deliver operational alerts to farmers. He’s recognized for mentoring junior engineers, managing GPU resources, and fostering cross-team collaboration, earning a peer-driven “One Team” award. Curious and experimental by training, he often merges statistical rigor with engineering pragmatism to tackle noisy, real-world data problems.
10 years of coding experience
13 years of employment as a software developer
Doctor of Philosophy - PhD Elementary Particle Physics, Doctor of Philosophy - PhD Elementary Particle Physics at University of Michigan
Bachelor's degree Physics, Bachelor's degree Physics at North Carolina State University
Analysis framework for producing flat ntuples (through skimming), making histograms, efficiency curves, and plots, e.g., generic histograms, efficiency curves, & Data/MC comparisons.
Contributions:2 releases, 40 PRs, 97 pushes in 10 months
skimmingpythonanalysisgenericanalysis-framework
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