Daniel Parshall is a Principal Data Scientist with 11 years of experience building production-grade ML systems that extract actionable insight from petabytes of endpoint and scientific data. He combines deep technical chops—from XGBoost, GANs and variational autoencoders to causal inference and differential privacy—with hands-on engineering of scalable data science infrastructure. Comfortable in customer-facing roles, he translates complex models into clear, decision-ready stories for product, sales, and executive stakeholders. His background as a physicist and instrument scientist gives him a rare expertise in wrangling very large, noisy experimental datasets and finding statistical leverage where others see only volume. At Lakeside he’s focused on “AI that speaks IT,” turning extremely high-frequency endpoint telemetry into operational visibility, and he’s driven open, impactful consulting work such as an open-source project for detecting familial links to combat fraud. Pragmatic and results-oriented, he consistently speeds onboarding, reduces operational cost, and delivers explainable, trustworthy analytics.
11 years of coding experience
8 years of employment as a software developer
Master’s Degree Engineering Physics/Applied Physics, Master’s Degree Engineering Physics/Applied Physics at University of South Florida
Doctor of Philosophy (Ph.D.) Physics, Doctor of Philosophy (Ph.D.) Physics at University of Tennessee, Knoxville
Contributions:9 pushes, 1 branch in 5 years 4 months
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