This past summer I worked at the World Bank’s Development Data Partnership, which works to create partnerships between private-sector data companies and international organizations for applications in development projects, including the World Bank’s projects. Specifically, I worked with a team based in India to develop a tool to predict precipitation-induced road closures based on characteristics of the road, the existing topography of the land, and real-time weather forecasts. The goal of this project is to ultimately determine which roads are most susceptible to flooding to inform both short-term emergency planning (ie. re-routing roads on a given day or sending a text alert to villagers) and long-term infrastructure planning (ie. to determine which roads need additional paving or drainage support). Through this work, I learned how to gather and select from often imperfect and incomplete data sources for development purposes, strengthen my data analytics skills on a large geospatial dataset, and lay the groundwork for a project that involves multiple stakeholders from both the private and public sector. From this experienc e, I was able to learn more about what careers in international development look like, the importance of granular, precise, and well-documented data in the development space, and the role of the private sector in collecting the data that can inform huge public sector projects and infrastructure policy in developing countries.