Jeffrey

Worked with both Department of Defense and United Health Group examining health claims, wrote code to break out patient chart xml files into panda data frames, wrote Python code with deep learning to turn recordings of meetings into meeting notes, and wrote code to search folders for identified word and list the documents and folders.

Built algorithms, used NLP to develop models, generated machine learning methodology, applied Graph theory, used fuzzy cluster analysis, retrieved data with Elasticsearch Developed algorithm, and used Cluster analysis.

Provided data science consulting services to Pfizer, Inc., Cox Communications, Unisys Corporation, and Walgreens. Established production of company-wide daily cash forecast using neural-net, random forest, time-series analysis, and naive Bayes, built big data storage in HIVE/Hadoop environment to handle large server log files for use in a Cybersecurity project, and developed predictive model from text mining techniques from manufacturing quality control documents to identify areas of improvement. Used text mining techniques – Term frequency/Inverse Term frequency, Topic modeling, Sentiment Analysis, and Word Association.