Outcome: Built and maintained operational analytics that enabled county leadership to monitor risk, prioritize action, and make timely decisions while reducing recurring reporting workflows from hours of manual effort to under two minutes through automation.

Decision Context

• Leaders needed reliable, up-to-date metrics to guide local response and planning
• Data arrived from fragmented systems with strict governance and privacy constraints
• Manual reporting pipelines were slow, brittle, and difficult to audit or reproduce

What I Led

• Operational decision support: Delivered clear, decision-ready summaries for mixed technical and policy audiences
• Automation & reliability: Replaced manual reporting with versioned, reproducible pipelines
• Risk identification: Used geospatial and trend analysis to surface emerging hotspots and equity gaps
• Short-horizon forecasting: Applied time-series and predictive methods to support local planning under uncertainty

Impact

• Enabled faster, more consistent decision-making across multiple public health initiatives
• Improved trust in reported metrics through reproducibility and data quality checks
• Freed staff time by eliminating repetitive, manual reporting workflows

Many operational datasets and internal analyses are not publicly shareable due to governance and privacy requirements. Public artifacts and code below illustrate transferable methods and system design patterns.