My position at Unum in Marketing Analytics gave me incredible insights in to how private industry handles data as compared to NASA. For my next position, I am looking to take on a more technical role with an emphasis on machine learning and pattern recognition, leveraging the experience I've gained at both Unum and JPL.As a Data Scientist at Unum, I developed and implemented Fuzzy Matching algorithm improvements which increased accurate company name matches by 10%, allowing more leads to be sent to sales representatives. I broadened my skill set to better align with the tech stack used in private industry, and quickly became versed in account based marketing, use of the Salesforce API, complex SQL queries, Azure Dev Ops, and many others.As a Staff Scientist at NASA’s Jet Propulsion Laboratory (JPL), my work focused on the collection and analysis of spatial image data to find low-strength signals and patterns, allowing broader trends to be detected. Using a combination of standard Python packages (e.g., numpy, scipy, matplotlib) and custom packages developed by our research team, I have written an extensive data analysis software suite in Python which used a novel fitting technique to characterize the data and find unexpected trends. The fitting software included binning of data, Gaussian fitting, outlier rejection via a clustering algorithm, and automatic comparison of the resultant data to physics-based dynamical models. I wrote the software in such a way that it could be run automatically on the entire data set as a script, generating visualizations of the results for each data point, and a collective presentation of the overall results.