About

Jinmei Zhang is a highly experienced Machine Learning Engineer with a strong background in computational chemistry. With a PhD in Physical Chemistry from Virginia Tech, Jinmei has a deep understanding of algebraic matrix/tensor equations and their implementation in quantum chemistry using C++. Jinmei's interest in machine learning algorithms grew during her projects, as she discovered that the underlying math is very similar to that used in quantum chemistry. Currently working as a Machine Learning Engineer at anomaly, Jinmei is passionate about learning new machine learning models and applying them in different projects. Her relevant experience of 7.79 years includes working as a Machine Learning Engineer at amplitude, where she performed in-depth data analysis on monitors and anomaly alerts with Python. She surfaced the data delay issue, which led to a large number of false anomalies, and worked with backend engineers to launch the delaying monitor feature and qualify its impacts. At circleup, Jinmei implemented a product categorization model, incorporated fine-tuned Bert models into brand resolution using text similarity and KNN search, and replaced entity extraction models with a recurrent neural network in Keras. She also optimized multiple tasks in Spark to decrease runtime and memory usage for larger datasets. Jinmei has built and assessed different models using XGBoost, LSTM, CNN, etc. for problems including text/image classifications and time series prediction. Jinmei's tech stack includes ML, Backend, Sagemaker, Apache Airflow, Python, TensorFlow, BERT, XGBoost, Keras, CNN, Spark, test, Neural Networks, Search, and Software Engineer. She has used best practices for software development, including git version control and test-driven development. Jinmei's education history includes a PhD in Physical Chemistry from Virginia Tech, a degree in Theoretical and Computational Chemistry from Shandong University, and a Bachelor of Science in Chemistry from Shandong University. Jinmei is a highly motivated, detail-oriented, and collaborative individual who is always looking to learn and grow in her field.