I am a highly accomplished machine learning and data scientist with more than 20 years experience. My passion is big data and surfacing the hidden intelligence within using techniques from machine learning, data mining, artificial intelligence and statistics. I am highly skilled in predictive modeling, classification, clustering, anomaly detection, data visualization, ensemble methods, information retrieval, cybersecurity analytics , NLP, recommendation models, and (one of my biggest loves) user behavioral analytics. I love to expand and customize existing algorithms to provide the most effective solution for the problem at hand. I am adept at building fast prototypes as a way to both assess and communicate the efficacy of a product solution. I am extremely self motivated and capable of working independently, as well as part of a larger cross functional team. I'm experienced and skilled at leading a team of scientists and engineers. I enjoy mentoring, as well as learning about new techniques and ideas. In summary, I am innovative, results-driven, and passionate about my work, and enjoy working with others who are like-minded. Data Science and Software Skills: • Predictive Modeling (support vector machines, random forests, CART, Bayesian models, linear and logistic regression, neural networks, kNN, XGBoost) • Clustering (k-means, DBSCAN, CLARA, Louvain Modularity, Expectation Maximization) • Anomaly Detection (distance and density-based, multivariate, clustering-based, parametric ) • Text analytics/NLP (categorization, clustering, concept/entity extraction, sentiment analysis, statistical semantics of phrases) • Search (ranking, relevance, query optimization) • R, Perl, Python, NumPy, Pandas, Scikit-Learn, C, C++, Java, Linux, Windows, Bash • Hadoop, HBase, Hive, MapReduce, Spark • SQL, MySQL, Elastic Search, MongoDB, ETL, REST, JSON • Gephi, Tableau, TensorFlow, MATLAB, Excel, PowerPoint, JavaScript, D3, SVG, HTML, CSS