PAUL SKEFFINGTON · MS · MPH
Paul Skeffington, MS, MPH
Public health data scientist and researcher working across public health informatics, GIS, biomedical data science, reproducible analytics, and responsible AI.
BACKGROUND
Public health, data science, and research engineering.
Paul Skeffington combines public health training with data science and research engineering. His public portfolio focuses on practical systems that make health, community, and research information easier to examine, reproduce, and review.
Current public work includes regional and municipal public-health dashboards, GIS-based community health analysis, water-access planning, biomedical and translational data science, signal-processing research, and evidence-governed uses of artificial intelligence.
Paul holds MS and MPH credentials and is engaged in graduate Health Data Science study at Dartmouth. Public-facing academic work emphasizes reproducible analysis, biostatistics, data visualization, longitudinal health data, and responsible use of AI in health research.
SELECTED PUBLIC WORK
Projects built around evidence and usability.
- JobHUD — a localized job-hunt board for public-health workers across the Northeast, with regional job discovery and decision support around compensation, benefits, commute, and role fit.
- Public Health HUD — a regional United States public-health dashboard organized around FEMA regions, source transparency, reporting dates, and coverage.
- Plymouth HUD — a municipal and community-health dashboard combining GIS, public-health data, infrastructure information, and source references.
- CART-TRACE — synthetic-first translational-health research focused on reproducible longitudinal clinical-data methods and post-infusion care trajectories.
- tech.fail — a cybersecurity incident and evidence surface focused on failures, breaches, outages, vulnerabilities, and source context.
- McDowell County water access — public-health and GIS planning work focused on water access and safety for older adults in McDowell County, West Virginia.
METHOD
Source-bounded, reproducible, reviewable.
The portfolio follows a consistent research approach: define the question, document the sources and assumptions, make the workflow reproducible, state limitations explicitly, and use human or independent review to guide the next step.
That same approach is applied to public-health dashboards, biomedical analyses, career decision-support tools, and responsible-AI systems.