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Melissa Cooper

Data Scientist and Machine Learning Engineer

Melissa Cooper is a highly skilled computer engineer who transitioned into a machine learning engineer role. With a deep understanding of data and a passion for applying flexible modeling and algorithm approaches, Melissa excels in empowering solutions through her expertise.

Her background in hardware engineering combined with intensive training from a data science bootcamp has equipped her with a strong foundation in machine learning. Melissa is adept at programming in various languages and is known for her results-oriented mindset and meticulous attention to detail.

Melissa's key specialties include Python, Linear/Logistic Regression, Classification, Random Forest, Neural Networks, Natural Language Processing, Deep Learning, Keras, Pandas, Numpy, Scikit-learn, Beautiful Soup/Selenium, MongoDB, SQL, Matplotlib, Seaborn, Tableau, Google Cloud, Git, Streamlit, Verilog HDL, Unix/Linux, and Xilinx Virtex FPGA products.

She holds a Master of Science (MS) degree in Electrical and Computer Engineering from Carnegie Mellon University, and a Bachelor of Music in Piano Performance from San Jose State University.

Throughout her professional journey, Melissa has served in various roles including Data Analyst at Car IQ Inc., Data Scientist at Metis, Piano Instructor in a self-employed capacity, and Systems Development Engineer at AMD.

Melissa Cooper
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Location

San Francisco, California, United States