About

Charlie Isaksson is a Principal Data Scientist with over 10 years of industry experience in Software Engineering and Data Science. He is an enthusiastic contributor who loves to learn and contribute to the team. Currently, he is serving at phData where he is implementing best practices in Data Science methodologies such as ETL technologies, data analysis, big data technologies, data management, predictive and exploratory model development, and data visualization. Charlie has proven success in the field of Data Science, leading multiple machine learning projects that include designing and developing large-scale machine learning models and open source software. He recently designed and implemented a deep learning framework that addresses many issues related to image model training and published numerous papers in the field of Data Mining. He has also developed an end-to-end data pipeline, ingestion, and aggregation of data and model development leveraging spark on Cloudera's infrastructure, automated the ML model and data by utilizing Oozie workflow, and used various interactive visualization techniques to provide customers with valuable insight into their large data. Charlie has experience in a broad range of tools and programming languages, such as R, Python, Hadoop, SQL, Hive, and Tensorflow. He is proficient in various exploratory and predictive techniques such as clustering, text mining, association rules, Markov model, k-nearest neighbor, support vector machine, XGBoost, convolutional neural networks, regression, and text classification. Charlie has also worked with databases such as DB2 and Oracle. Charlie has previously worked as a Principal Data Scientist at Securonix, where he utilized deep learning techniques to detect homoglyph attacks by converting text domain names to images. He also optimized the threat model for automated detection of cyber threats, including indicators of compromise and email spam. Prior to that, he worked as a Data Scientist at State Farm, where he led projects in text mining, data mining, deep learning, and numerous cost-savings initiatives. Charlie holds a Doctor of Philosophy (Ph.D.) in Data Mining from Southern Methodist University and an M.S in Computer Science from Mid Sweden University. He is proficient in various technologies such as Docker, Neural Networks, ETL, infra, Apache Hive, Scala, Java, and has relevant experience of 7.12 years.