Lucas Zhang

Lucas Zhang has been working in the data science field since 2018. In that time, they have held various roles at different companies. In 2021, they began working at SentiLink as a Data Scientist & Sales Support Team Lead and a Data Scientist. In 2019, they began working at Paul Fredrick as a Data Scientist where they developed machine learning and deep learning models from scratch to drive sales and improve marketing campaign efficiency. Lucas also uncovered critical business insights and solved complex problems to support business strategies through data mining. In the same year, they also worked at The Johns Hopkins University as a Data Science Intern, where they developed a machine learning model to predict incidence and severity of acute kidney injury after admission. In 2018, they worked at SECU Credit Union as a Data Insights Intern, where they developed a Natural Language Processing algorithm to clean raw merchandise name in transaction data with 0.93 accuracy. Lucas also conducted membership retention analysis to uncover root causes of low retention rate in previous years and supported management decision making. Lucas also worked at The Johns Hopkins University in 2018 as a Data Scientist Intern, where they developed hierarchical multivariate survival models to study risk factors of mortality on medical specialist waiting lists and published a research paper in BMC Public Health in February 2019.

Lucas Zhang obtained their Bachelor's Degree of Science in Mathematics from Beihang University in 2017. Lucas then went on to pursue a Master's degree in Applied Mathematics and Statistics from Johns Hopkins Whiting School of Engineering, which they obtained in 2019. In addition, Lucas has obtained several certifications from Coursera and Triplebyte, including a Natural Language Processing Specialization, a Triplebyte Certified Machine Learning Engineer, Convolutional Neural Networks, a Deep Learning Specialization, Sequence Models, Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization, Neural Networks and Deep Learning, and Structuring Machine Learning Projects.