Data Analyst · Toledo, Ohio
I turn messy operational data into clear insight for mission-driven teams — with a background in community health, UX design, and field operations shaping every analysis.
Current Stack
Focus Areas
I'm a data analyst building toward work that actually matters. My path here wasn't a straight line — I came through community health navigation, conversational UX design, and field operations — and that background shapes how I approach data: practically, with real-world context, and always with people in mind.
I use Python and SQL to clean datasets, explore patterns, build predictive models, and create visualizations that help teams make confident decisions. I'm currently completing a Data Science & Machine Learning certificate at TripleTen (expected April 2026), where I'm deepening my applied analytics and modeling work.
I'm most interested in roles where data supports research, program evaluation, or mission-driven innovation — especially in healthcare, education, and community-focused organizations. I care less about impressive-looking dashboards and more about insights that actually inform decisions for people who need them.
Organizations lose members, patients, and students before they ever ask for help. Built a classification model to identify who's at risk early — so teams can act before disengagement becomes departure. Addressed real-world class imbalance, optimized for precision-recall balance, and focused the output on decision support rather than model performance for its own sake.
View on GitHub →When services are under-resourced, the people who depend on them most feel it first. Built a time-series forecasting model using historical demand patterns to anticipate peak usage periods — giving operations teams the information they need to plan staffing, allocate resources, and improve access before problems emerge. Hit an RMSE target of ≤ 48.
View on GitHub →Most organizations collect more qualitative feedback than they can ever manually read. Built an NLP pipeline to classify and surface patterns from large volumes of unstructured text — turning open-ended responses into structured insight. Achieved F1 above 0.85 across multiple model approaches. Applicable anywhere teams need to understand what people are actually saying: surveys, program evaluations, community input.
View on GitHub →Languages
Libraries & Tools
Analysis & Modeling
Domain Knowledge
I'm currently open to data analyst roles at mission-driven organizations — particularly in healthcare, education, or community-focused work. If you're building something that matters and need someone who can make data useful, I'd love to hear from you.
The goal is to turn data into information, and information into insight.
— Carly Fiorina