A typical day involved working with exported mailing datasets, organizing data in Excel, generating reports, and presenting findings through slideshows and summaries. At the beginning of the internship, I spent time learning the company’s workflow, databases, and reporting systems. My official responsibilities included creating data reports and analyzing mailing output data across industries. This included forming presentations and summaries from exported datasets for the marketing team. While working on these reports, I quickly noticed that the underlying database system was outdated and inefficient. Exported CSV files contained inconsistent date and number formatting, arbitrary row and column placement, and required manual restructuring before the data could be analyzed or converted into Excel PivotTables.
After noticing the inefficiencies in this process, I started (to the excitement of my supervisor) to develop an automation tool to eliminate much of the repetitive manual work. Using Python and the Pandas library, I created an application that extracted raw datasets from the database and automatically converted them into formatted Excel outputs with customizable inputs and automatic field detection. The tool significantly reduced the time analysts spent manually preparing data. One major challenge was that company devices were heavily restricted, and I was not allowed to install an IDE or code editor such as VSCode. As a result, I wrote and debugged the entire application using Notepad. Although frustrating at times, this forced me to better understand my code’s structure and adjust my debugging process without the ability to rely on external tools.
One of the most important things I learned from this practicum was the value of analytical thinking and initiative in technical environments. In my mathematics and computer science courses, problems are often presented with a known problem, known data, and an expected solution. In the workplace, however, the problem itself may not initially be obvious. My automation project was not formally assigned to me. Instead, I identified inefficiencies through observation, then proposed and implemented a solution independently. This experience reinforced ideas from my computer science coursework related to logical problem decomposition, pattern recognition, and systematic thinking.
Another lesson I learned was how science and technical skills connect with broader organizational and social systems. Before this experience, I mainly viewed programming as an insular process removed from its greater use; but through this experience, I saw just how intricately different departments interacted, where producing workflow improvements on the programming side could impact communication, efficiency, and decision-making across multiple teams. My automation tool reduced tedious manual labor, allowed analysts to process larger datasets more effectively, and contributed to improving the quality of mail campaigns while reducing unnecessary or spam-like mailings. This experience demonstrated how programming can be used in many contexts to radically improve workflows and remove tedium from the equation entirely.
The internship also affected my future academic and career plans. Although I enjoyed the programming and automation aspects of the work, I do not want to orient my technical background primarily toward marketing or business analytics. However, the experience also strengthened my interest in software development and applied mathematics. It showed me how much joy it brings to build tools that solve practical problems, especially when I have the freedom to take initiative and design solutions independently. Since leaving the internship, I applied to the Computer Science LEP. I await a response to determine if I will pursue a double major in this field.
The internship also helped me develop communication and collaboration skills. Although much of the programming work was independent, the project required regular interaction with analysts, developers, and leadership, namely my advisor Siri Prax and coworker Michelle Chione. I frequently gathered their feedback, refined the application based on how they would use it, and eventually presented the completed project to company leadership. Within the group, I found myself naturally taking on the role of an initiator and technical problem-solver. This was somewhat familiar to me academically, but the professional environment required clearer communication and greater adaptability than the projects I’m used to.
If I could better prepare for this experience, I would have pursued more software engineering coursework earlier, particularly related to interface design, version control systems, and collaborative development practices. While I had programming experience beforehand, much of my knowledge was theoretical or classroom-based. The internship exposed gaps between academic coding assignments and building tools intended for real users in professional settings.
Overall, I wouldn’t likely recommend this specific opportunity to future SGC students, because of the way I was forced to set my own goals without direction, however applying STEM skills in an unrelated industry is generally something I would recommend. An experience like my practicum would be especially helpful to those interested in applying technical skills in interdisciplinary environments. Most importantly, experiences like mine demonstrate the importance of initiative. While I wasn’t explicitly tasked with building an automation system, I identified a problem. I developed a solution independently, contributing more meaningfully to the organization and to my learning than I would have through assigned tasks alone. I also gained confidence in my ability to learn new technologies, communicate with technical and non-technical teams alike, and complete large projects from beginning to end. Overall, the practicum gave me valuable insight into professional technical work and helped clarify the kinds of environments and projects I hope to pursue in the future.
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