Bianca Schutz
Hi! I'm Bianca, a Rice University Political Science graduate and current data science student at UT Austin. I bring experience communicating data and building tools for non-technical audiences. Data that can keep policymakers accountable or highlight disparities in communities are often difficult to interpret, limiting the impact they can have. I'm passionate about making this information more accessible through clear, actionable stories that drive policy change.
Currently
Master of Science in Data Science Student at the University of Texas at Austin
In my free time, I'm enjoying hiking in the Alps on weekends and have recently started indoor climbing and distance running with my friends. I'm also practicing photography and as a data-related project, working on building Mandate.
Featured projects
After hearing frustrations of early-career hoping to pursue opportunities in the UN system on how difficult it is to keep track of what jobs are open, I'm currently working on a website to fill this gap. From regularly obtaining job listing data from various UN, IO, and NGO agencies to putting it all in one place, this project follows an earlier version of a Shiny App I'd created, where I manually added listings I found across different organizations.
Last fall, I created a simple Streamlit application (see my original blog post about weightlifting for more information) that required me to manually create CSV files to upload data to get my insights. Using the Hevy and Strava APIs, this web app uses webhooks to get my insights directly from the source and allow me to look ahead and plan my workouts better. Both Strava and Hevy have great functionalities for running and lifting respectively, but this combines them into one spot with the insights I'm more interested in.
In Spring 2025, I partnered with the Kinder Institute’s Houston Education Research Consortium and the Data to Knowledge Lab to make school district data more accessible for district administrators. Worked with a team of undergraduates to build a nearest neighbors framework to identify demographically similar school districts, enabling meaningful comparisons. Developed a Shiny for Python dashboard where users can select demographic criteria, view their closest-matching districts, and compare key performance outcomes.
