Inactive

The purpose of this requirement is to enable senior leaders to focus on better outcomes by revolutionizing how enterprise analytics is integrated with cross functional research. This requires a multi-... The purpose of this requirement is to enable senior leaders to focus on better outcomes by revolutionizing how enterprise analytics is integrated with cross functional research. This requires a multi-disciplinary approach in bringing together expertise from both social science and applied mathematics to define research questions from motivating problems, gather the appropriate enterprise data, explore the data, build and refine analytic models before providing input for resourcing decisions. In order to increase the capabilities and analytic capacity, FPD seeks to leverage cutting-edge techniques of applied mathematics through the use of AutoML tools, which streamline several facets of the machine learning process that ordinarily require highly trained data scientists. The particular phases of the machine learning and data science application which must be streamlined include: feature selection, feature preprocessing, feature engineering, model training and selection, and parameter optimization.