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@calebfox
PhD statistician and data scientist specializing in statistical modeling and data driven insights
United StatesI am a PhD candidate in statistics conducting research on bootstrap methods for spatiotemporal Hawkes process confidence intervals while mentoring junior analysts and consulting on applied projects. I combine rigorous statistical theory with practical data science to deliver reproducible, actionable results.
My consulting work includes creating a novel player-value metric and PowerBI dashboard for collegiate athletics, demonstrating cost and visit reductions for AIRROSTI using propensity-score weighted regression, and publishing groundwater research in Frontiers in Earth Science; I also interned in business insights at Eli Lilly using R, SQL, and AWS to inform marketing strategy.
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Work history, roles, and key accomplishments
ECDeveloped a custom evaluation framework using survival analysis and high-dimensional similarity metrics to identify over- and under-targeted users for Verzenio, enabling more efficient personalized outreach. Analyzed large-scale marketing data with R and SQL on AWS and presented findings to cross-functional stakeholders.
Degrees, certifications, and relevant coursework
BUDoctor of Philosophy, Statistics
2021 -
Doctoral research on bootstrap methods for spatiotemporal Hawkes process confidence intervals; expected completion December 2025.
BUMaster of Science, Statistics
2021 - 2022
Grade: 3.86/4.00
Completed Master of Science in Statistics in December 2022 with a 3.86/4.00 GPA.
TUBachelor of Science, Mathematics
2017 - 2021
Grade: 3.88/4.00 (Magna Cum Laude)
Activities and societies: Taylor University Baseball Team; Taylor University Track & Field; Leadership Programming Cabinet; Teaching Assistant; Mathematics Tutor; Discipleship Assistant
Bachelor of Science in Mathematics with minors in Computer Science and Data Science; graduated May 2021 with a 3.88/4.00 GPA (Magna Cum Laude).
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