Bright Tsevi

Bright Tsevi has a diverse work experience, ranging from Data Engineering to Teaching and Research Assistance. In 2022, they began working as a Data Engineer at Arine. In 2021, they were a Data Engineering Intern at Arine, where they developed and deployed ETL pipelines to clean data for dash-boarding and reporting, created live dashboards in AWS Quicksight to visualize and report patient data, and created glue jobs in AWS to scrap and update data in tables for live dashboards. In 2019, they were a Graduate Research Assistant at Norfolk State University, where they implemented an LSTM (Long Short-Term Memory) model to detect and classify types of Atrial Fibrillation, and developed a computational model for Human Induced Pluripotent Stem Cell (HiPSC) to study various cardiac mutations. Bright was also a Data Science for All (DS4A) Fellow in 2021, where they were accepted into a 14-week training program in practical data skills. Bright was a Graduate Mentor at Norfolk State University in 2021, a First Place team winner in the first edition of JPMorgan's Data for Good Hackathon in 2021, a Service & Calibration Engineer and Sales Engineer at Automation Solutions Limited in 2017, a Teaching and Research Assistant at Kwame Nkrumah'\u200b University of Science and Technology, Kumasi in 2016, and an Intern at Electricity Company of Ghana in 2015.

Bright Tsevi has a Master of Science in Electrical and Electronics Engineering from Norfolk State University (2019-2021). Bright also holds a Bachelor's Degree in Electrical, Electronics and Communications Engineering from Kwame Nkrumah' University of Science and Technology, Kumasi (2012-2016). Additionally, Bright has a Certificate in Data Science from Netflix Data Science Boot Camp (2021). Bright has also obtained various certifications from Coursera, IBM, and LinkedIn, including AWS Cloud Practitioner Essentials, Hadoop 101, Power BI Top Skills, Reinforcement Learning Foundations, Business Analytics: Understanding and Using Confidence Intervals, Statistics Foundations: 1, 2, and 3, Machine Learning, Data Analysis Using Python, Named Entity Recognition using LSTMs with Keras, and Python for Data Science.