Khush Shah
About Khush Shah
Khush Shah is a student tutor and data science professional with a strong background in machine learning and cloud services. He has completed internships at Adani Group and worked as a Site Reliability Engineer at Crest Data Systems, while currently pursuing a Master of Science at The George Washington University.
Work at The George Washington University
Currently, Khush Shah holds the position of Student Tutor I Non-FWS at The George Washington University. He has been in this role since 2024, contributing to the academic support of students in the Washington DC-Baltimore Area. His responsibilities include assisting students with their coursework and providing guidance in various subjects.
Education and Expertise
Khush Shah is pursuing a Master of Science in Data Modeling/Warehousing and Database Administration at The George Washington University, with an expected completion date in 2025. He previously earned a Bachelor's degree in Electrical, Electronics, and Communications Engineering from Nirma University in 2023. His educational background is complemented by proficiency in machine learning frameworks such as PyTorch, TensorFlow, and sci-kit-learn, as well as familiarity with cloud services, particularly AWS.
Background in Data Science and Research
Khush Shah has gained practical experience in data science through internships at Adani Group. He served as a Business Research Intern in 2021 and as a Data Science Intern in 2022, where he conducted research projects focused on blockchain and renewable energy certificates. His experience includes utilizing tools like Orange and Power BI for data analysis and visualization.
Experience in Site Reliability Engineering
In 2023, Khush Shah worked as a Site Reliability Engineer at Crest Data Systems for five months. In this role, he focused on performance monitoring and system optimization using Splunk Enterprise. His technical skills include a deep understanding of database systems such as MySQL, MongoDB, and Neo4j, as well as practical experience in MLOps and DevOps methodologies.