Nicholas Arner
About Nicholas Arner
Nicholas Arner is a Senior Data Scientist currently working at Amentum, with extensive experience in statistical analysis and data science across various organizations in the Washington DC-Baltimore area. He has held several roles, including Senior Statistical Analyst at Metronome and Assistant Technical Lead at KeyLogic, and has a strong academic background in Political Science and History.
Current Position at Amentum
Nicholas Arner serves as a Senior Data Scientist at Amentum, a role he has held since 2023. In this position, he focuses on delivering data-driven insights and solutions to clients. His responsibilities include statistical analysis, data modeling, and the implementation of agile methodologies to enhance project outcomes.
Previous Experience at Metronome
Prior to his current role, Nicholas worked at Metronome for five years, serving as a Senior Statistical Analyst and Data Scientist from 2018 to 2023. In this capacity, he contributed to various data analysis projects and collaborated with teams to develop statistical models and insights for decision-making.
Education and Academic Background
Nicholas Arner holds a Master of Arts in Political Science and Government from George Mason University, where he studied from 2009 to 2011. He also earned two Bachelor of Arts degrees in History and Political Science and Government from St. John Fisher University, completing his studies from 2003 to 2007.
Professional Contributions and Collaborations
Throughout his career, Nicholas has collaborated with the Human Capital Office to support the Office of the Director of National Intelligence (ODNI). He has led the design and implementation of multiagency surveys aimed at enhancing customer centricity within the intelligence community, showcasing his ability to address complex workforce research questions.
Technical Skills and Methodologies
Nicholas incorporates agile methodology into his work, focusing on delivering mission-critical solutions. He regularly updates and designs SPSS syntax and coding to facilitate comprehensive analysis and documentation for hypothesis testing, ensuring robust data interpretation and reporting.