Enterprise Data Scientist responsible for leading the data science thrust and overseeing activities of that ensure the proper execution of machine learning and algorithm development. Routinely creates new data sciences capabilities for the enterprise by envisioning and executing strategies that will influence improvement of organizational performance by enabling informed decision making. Proactively works with business executives and various departmental heads across the enterprise in order to provide advanced analytic data modeling systems. Leads the development of big data capabilities as well as the coordination of cross-functional analytic initiatives. Designs and launches innovative and complex analytic models, utilizing a blend of contemporary and traditional data mining techniques, applied to both structured and unstructured data sets.

Douglas established the Data Science team for Langley Federal Credit Union combining a mixture of Analytics Developer, ETL Developer, and Applications Developer positions. Notable achievements by the team include: Direct Deposit Prediction via Binary Logistic Regression, Member Engagement Forecasting via Simple Moving Average, Automation of Payment Posting via PowerOn, Automation of Credit Card and Auto PreApproval via C# and R.Simultaneously the team improved data quality within the Data Warehouse to allow for computation of all Deposits and Loans by each individual member (300K+). As the first Data Science Manager at the credit union, Douglas identified key areas that would increase productivity and growth for the organization which included the organization's first implementation of Agile methods for development. He also reorganized the business unit and handpicked key employee hires to use for the Data Science team (five direct reports). Douglas trained engineers and computer scientists on Machine Learning, Data Mining, Business Intelligence Reporting, and success sharing measures.

Douglas served as the Program Manager for all NASA Langley business at Booz Allen. Douglas led a team of roughly 16 staff in Engineering, IT, Applied Mathematics, Computer Science, Database Management, and Data Science that delivered consulting expertise to NASA Langley. Some notable accomplishments include:Applications of a multivariate predictive risk algorithm co-created by Douglas called, MRisk. From 2011 to 2018, Douglas led a team that provided risk and issue management support for flight demonstration projects within the Aeronautics Research Directorate (ARD). The team developed and documented an organized, comprehensive, risk database using VBA and Python to handle 200+ risks simultaneously across the portfolio of projects and report on those risks on a monthly cycle. The teams work has been featured at Systems Engineering symposiums.From 2012 to 2016, Douglas led a team that has developed the foundational capability of applying big data technologies to selected pilot projects for NASA Langley in the domain of aerospace data for Langley’s OCIO. The Big Data pilots are in the areas of automated anomaly detection, predicting flutter conditions, better turbulence modeling from computational and experimental data, better air traffic management, and space radiation data mining. Prior to pilot formulation the team wrote the organization’s strategic plan for Big Data Science.From 2011 to 2018 Douglas led a team that provided solutions architecture, configuration management, and web development for the Atmospheric Science Data Center (ASDC). The team implemented an Open-source Project for a Network Data Access Protocol (OPeNDAP), Web Mapping Service (WMS) and ArcGIS web services for enhanced access to ASDC hosted data, facilitates and coordinating open-Geospatial Intelligence Services (GIS)/Geospatial Data Abstraction Library (GDAL) framework and plugin development.

Fostered an understanding of mathematics including Calculus and Statistics to students from diverse educational backgrounds. Notable accomplishments included:Teaching elementary statistical methods and concepts including visual data presentation, descriptive statistics, probability, estimation, hypothesis testing, correlation and linear regression. Helped students develop statistical thinking, learn simulation analysis, and the use of statistical software such as STATA, TI-83+, and Excel.Teaching Pre-Calculus topics including power, polynomial, rational, exponential, and logarithmic functions, and systems of equations and inequalities. Teaching College Algebra which emphasized performing operations on exponential expressions and polynomials, factoring polynomials, solving polynomial equations, simplifying rational algebraic expressions, solving rational algebraic equations, simplifying radical expressions, using rational exponents, solving radical equations, working with functions in different forms: ordered pair, graph, and equation form. Helped students to master all the different factoring methods, and applied problems using polynomial expressions, rational and radical equations.

Analyzed business issues to develop and execute study plans that result in recommendations to maximize operational efficiency, quality and compliance.Douglas completed the mathematical analysis on a study of Embedded Training in the Army. Analysis techniques included simulation analysis, integer programming, and cost analysis. Calculated the life-cycle cost of using various embedded training strategies. The resulting recommendation saved the Army around $900 million.Douglas served as project lead for a case study on the Operations & Maintenance (OMA) costs of training in the Army. The study involved formal Hypothesis Testing, Regression analysis, data mining techniques, and Monte Carlo simulation. Douglas proved that it was possible to accurately predict the cost of training in the Army and show its effects on readiness. This notion was never before proven. The study led to an Army-wide effort to calculate OMA cost of training and identify areas of critical funding by the VSCW MDEP. Douglas’s model was featured at the 2004 Range Symposium in Chesapeake, Va where he led a campaign to recruit more than 50 installations across the Army to participate in the study to improve the funding of Army ranges. The study developed a predictive model on the cost of training on all Army ranges and effectively correlated the effects of funding on the Army’s ability to train and prepare itself for war.