Morgan Askins is a Machine Learning Data Scientist with 12 years of experience applying statistical modeling and optimization to real-world systems, transitioning from particle physics research to logistics and warehouse operations. With a PhD in physics, Morgan built high-throughput ML inference pipelines and open-source tools for Bayesian analysis and simulation, achieving dramatic speedups (1000x in one fit) and deploying models that handle thousands of samples per second. At Stitch Fix they boosted warehouse labor efficiency by 30% via optimized worker pathing and reduced inventory holding time by four days through dynamic order-to-warehouse assignment. Comfortable across the stack—from Julia and TensorFlow to Streamlit and Looker—Morgan blends deep scientific rigor with pragmatic engineering to deliver measurable operational impact. A distinguishing trait is their ability to translate complex probabilistic methods into production-grade systems and intuitive dashboards that empower non-technical stakeholders.
12 years of coding experience
15 years of employment as a software developer
Doctor of Philosophy - PhD, Physics with an emphasis on nuclear science, Doctor of Philosophy - PhD, Physics with an emphasis on nuclear science at University of California, Davis
Bachelor of Science - BS, Physics, Bachelor of Science - BS, Physics at Florida State University
Contributions:15 pushes, 2 branches in 4 years 4 months
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