About

Nathan P. is a skilled software engineer with a passion for data science and backend/server technologies. With his problem-solving mindset, Nathan has built up his skills in coding, computer science, data analysis, and information systems. He believes that learning advanced technologies is a means to an end to solve complex issues faced by consumers and business communities, which has made him a much more creative problem solver. Nathan has developed skills in web development, computer science, and data science. His web development skills include Flask, NodeJS, MongoDB, JavaScript, Sockets, HTML, CSS, API, and Django. In computer science, he has expertise in algorithms, data structures, UNIX utility, and version control (Git). In data science, Nathan has experience in machine learning, deep learning, data analysis, natural language processing (NLP), Keras, Scikit-learn, Pandas, SQL, and NoSQL. Currently working as a Software Engineer and Data Scientist at General Dynamics Mission Systems, Nathan has also gained valuable experience as a Machine Learning Engineer Intern at Thoughtexchange. His responsibilities included conducting research and implementing a system to accurately predict the probability of toxicity in pieces of text. Nathan conducted data analysis, data visualization, and data cleaning on over 100,000 data samples. He built deep learning models, including Bi-LSTMs, CNNs, and Attention Mechanisms, and optimized the best performing models based on the desired balance between recall and precision metrics. Nathan has also worked as a Software Engineer Intern at the University of San Francisco (USF), where he created an easy-to-use data querying tool for non-technical users from USF geospatial research teams and other graduate students. He connected a web user interface with a RESTful API that fetched custom requests from the Google Earth Engine API and served JSON data back to the client. Nathan also wrote custom documentation for the API that he built. Nathan holds a Bachelor of Applied Science (BASc) in Computer Science from Make School, where he graduated in 2020. With his expertise in research, data science, software engineering, ML, NLP, deep learning, web development, SQL, Keras, Scikit, Javascript, Pandas, NoSQL, MongoDB, Flask, NodeJS, and Django, Nathan is a valuable asset to any team looking to solve complex problems.