Public health data scientist building field-ready data systems
I build data workflows for global health programs: ODK pipelines, QA checks, and reporting that holds up in the field.
What I focus on
Field-first data systems
Building forms and pipelines that hold up under real field conditions. Spotty connectivity, training gaps, and messy edge cases are the baseline.
Data quality & governance
QA checks, validation rules, and reproducible cleaning scripts to keep program data trustworthy across every reporting cycle.
Analytics + geo
R-based analysis and publication-ready geospatial maps. Taking noisy operational data and turning it into something a program can actually act on.
Currently
Building data infrastructure for Guinea Worm surveillance at the Carter Center. ODK pipelines, geospatial reporting, and bilingual training materials.
Selected work
Projects
A few things I've built across field operations, surveillance systems, and data automation.
Guinea Worm Program: ODK → QA → reporting pipeline
Built and maintained a production data workflow for Guinea Worm eradication operations, connecting field data collection to standardized outputs.
508 PDF automation & multilingual reporting (Cloudburst Group)
Automated PDF accessibility remediations and translation workflows to streamline compliance and reporting across international development projects.
MEWS Evaluation (MPH capstone): climate-informed outbreak risk
Evaluated and improved an early warning approach for meningitis outbreaks using climate data and operational constraints.
Data quality audits at village scale
Performed deep-dive audits on national datasets to identify structural issues that can silently corrupt analysis.
Open to roles
Data science, analytics engineering, public health data
I work across the full pipeline, from data collection through to reporting. If you need someone who can own that end to end, reach out.