Greg Burgess
Analytics Consultant (Contractor), Pharmacy Personalization And Experimentation Team
Greater St. Louis United States
Summary
👤
Senior🎓
Top SchoolGreg Burgess is an analytics consultant and former senior scientist with 8+ years translating complex experimental and neuroimaging data into actionable business and research decisions. He combines a Ph.D. in Psychology with practical data science skills—Python, SQL, A/B testing, ML, and pipeline engineering—to design experiments, clean and model large datasets, and communicate results to technical and non-technical stakeholders. At Washington University he led multi-site data curation and QA efforts, mentored research teams, and helped adapt HCP processing pipelines (notably TaskfMRIAnalysis) to newer toolchains—work he continues to apply today in CVS Health’s personalization and experimentation group. Intensely curious and methodical, he excels at identifying the critical questions, building reproducible analysis pipelines, and persuading teams with clear evidence. He brings a rare blend of academic rigor in fMRI/behavioral science and hands-on engineering that bridges research-grade data practices with production experimentation.
8 years of coding experience
22 years of employment as a software developer
B.S., Psychology, B.S., Psychology at Penn State UniversityData Science, Data Science at Flatiron SchoolPh.D., Psychology, Ph.D., Psychology at Washington University in St. Louis
Github Skills (3)
fs10
bash10
shell10
Programming languages (2)
Github contributions (5)
Washington-University/HCPpipelines
Apr 2018 - Jul 2021
Processing pipelines for the HCP
Role in this project:
Back-end Developer
Back-end DeveloperContributions:27 reviews, 17 commits, 3 PRs in 3 years 4 months
Contributions summary:Greg primarily focused on modifying the TaskfMRIAnalysis scripts, which are likely part of the processing pipelines for the Human Connectome Project. Their contributions involved adapting the scripts to newer versions of FSL, particularly by changing how temporal filtering is implemented. Furthermore, the user made improvements to the scripts by adding and clarifying comments and consolidating individual commands into functional blocks. The user also implemented lowpass filters in file and directory names.
hcp-pipelinespipelinesshellhcp
twood2015/Flatiron_Project_1
Mar 2022 - Mar 2022
Data science project #1
Contributions:19 PRs, 27 pushes, 2 branches in 4 days
pythonsciencedata-sciencemachine-learningdata-science-project
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