Nicholas D.
About Nicholas D.
Nicholas D. is a Lead Data Engineer at the Federal Reserve Bank of New York, specializing in distributed high-performance computing and statistical modeling. He holds degrees in Geophysics from Georgia Institute of Technology and Computer Science with a focus on Machine Learning from Stanford University.
Work at Federal Reserve Bank of New York
Nicholas D. serves as the Lead Data Engineer at the Federal Reserve Bank of New York, a position he has held since 2022. In this role, he focuses on distributed high-performance computing and statistical modeling. His responsibilities include leveraging cloud computing platforms and big data technologies to enhance data processing and analysis capabilities within the organization.
Previous Experience at Federal Reserve Bank of Kansas City
Before joining the Federal Reserve Bank of New York, Nicholas D. worked as a Data Scientist at the Federal Reserve Bank of Kansas City from 2019 to 2022. During his tenure, he applied his skills in data analysis and statistical modeling to support various projects and initiatives within the bank.
Education and Expertise
Nicholas D. studied at Stanford University, focusing on Computer Science and Machine Learning from 2018 to 2019. He also holds a Bachelor of Science degree in Geophysics, Earth and Atmospheric Sciences from the Georgia Institute of Technology, where he studied from 2013 to 2017. His educational background provides a strong foundation for his expertise in distributed computing and statistical modeling.
Technical Skills and Proficiencies
Nicholas D. possesses proficiency in various cloud computing platforms and big data technologies, including AWS, Cloudera, and Hadoop. He is skilled in programming languages such as Solidity, MATLAB, and Julia. Additionally, he has experience with data visualization tools like Plotly and D3.js, and is knowledgeable in containerization and orchestration technologies, including Docker and Kubernetes.