As lead engineer within HOXAR’s Applied Analytics, Statistics and Machine Learning Team you will work with large structured and unstructured datasets to transform data for more effective decision support through data collection, data analytics, data mining, clustering analysis, pattern recognition and extraction, automated classification and categorization, and entity resolution to implement and enhance automated risk assessment capabilities.
Qualifications
- At least 2-3 years of experience in leading advanced analytics teams to solve complex problems.
- Proficiency in R, Python, Java and SQL languages as well as statistical software packages such as SAS, SPSS Modeler, or equivalent.
- Data and Database Experience: Development, Extraction, Translation and Load (ETL), building structured and unstructured datasets, data mining, training set construction.
- Experience processing large-scale data sets (Hadoop, HBase, Pig, etc.).
- Machine Learning: Unsupervised (Cluster Analysis [K Means, Hierarchical, Deep Belief Networks, Principal Component Analysis], Segmentation); Supervised (K-Nearest Neighbor, Random Forests, [Boosted] Decision Trees, Support Vector Machines, Logit, Regression, Rotation Forests, Ensemble, Categorization, Classification).
- Neural network, Bayesian Networks, Dempster-Shafer, support vector machines, decision trees, random forests); Semi-supervised machine learning.
- Pattern recognition and extraction, automated classification, categorization and entity resolution of multimedia data (text, audio, image, video, etc.).
- Bachelor’s Degree (required), Master’s or Ph.D. degree (preferred) in operations research, industrial engineering, mathematics, statistics, computer science/engineering, and other related technical fields or equivalent practical experience.