-Led innovation team as sole data scientist in creation of Machine Learning model which classified likelihood of insurance payments on patient accounts. Model had a payment prediction accuracy rate of >80% across 10 hospital systems. Efforts including comparing performance of K-Nearest Neighbor, Logistic Regression, Decision Tree and Random Forest algorithms.-Created a SDoH recommender engine combining providers' RCM data and social discriminant data to recommend patients who had a high potential of 90 day readmission.

- Data scientist in Data & Analytics Innovation team in charge of applying advanced statistical methods to immense clinical and financial healthcare data sets while leveraging the latest analytical tools such as Python, R and Hadoop-based applications such as HIVE and PIG- Created forecast model that predicts the payment hierarchy trends in the United States (Q1 2011 - Q1 2015) by comparing delinquencies between patient medical payments versus home loan, primary vehicle and credit card payments.- Lead data analyst and designer of prototype product focusing on data mining of billing notes in hospital’s revenue cycle department IT system in efforts to identify claim leakage accounts. Product also allows clients easy comparison across 16 HFMA map keys with the ability to drill down views across different payers, CPT codes and service departments.- Department lead BI/Tableau developer with a deep passion for data visualization. Created, published and maintained entire product via dashboards on Tableau Server which are viewed weekly by senior leadership in TU Healthcare and clients' head of Revenue Cycle.

-Member of three person pilot program comprising of graduate students in analytics that introduced data mining concepts and solutions to the Global Services and Solutions team-Created a predictive model in SAS-Enterprise Guide that forecast wheel removals for BNSF locomotives by utilizing 5MM observations from multiple sensor and ADW source data sets resulting in 39% improvement over prior forecast analysis-Enriched existing fuel efficiency model by reproducing model functionality in SAS- reducing operational time by 80%. Model processed 44MM records from trip-optimizer, fuel and track data sources. Project compared results between KNN, K-Means, Random Forest and Neural Network models-Leveraged use of graphical libraries in R-Studio and SAS-Enterprise Guide for advanced data visualization for both univariate and multivariate datasets-Prepared presentations and conducted meetings expounding project goals and results to C-level executives in bi-monthly meetings

-Graduate with Distinction in Predictive Analytics acquiring skills in the ability to mine hidden gems located within large sets of structured, semi-structured and unstructured data. Able to leverage a heavy dose of mathematics and applied statistics with visualization and a healthy sense of exploration.-Heavy use of statistical programming applications such as SAS, R, Python and SPSS; utilized on both supervised and unsupervised modeling methods -Created several recommender and predictive modeling systems using machine learning algorithms (Decision trees, k-NN Linear regression, Naive Bayes, Logistic regression, and Support vector machine (SVM))-Leveraged the use of dimensionality reduction to improve analysis of very large datasets (CCA, Principal Component Analysis)-Created virtual node clusters using IBM SmartCloud while also creating Hadoop framework. Set up multiple working Hadoop systems using Putty while employing knowledge of HDFS architecture

Major Projects: The Boeing Company 787-8 Dreamliner, Lockheed Martin F-35 Joint Strike Fighter, Airbus A350XWB- Designed and qualified control systems solutions for military and commercial electric systems, focusing on Primary and Secondary Power Distribution Systems- Critical on-site technical and program engineer in Paris, France working on complex multi-year product launch with expected 20-year company revenue of $15B- Led multi-nationally based team through complex, federal regulation process for product certification focusing on requirement traceability and environmental qualification based on DO-160 standards for avionics hardware for both Safety of Flight and Certification milestones- Advised System and Program Engineers from The Boeing Company and Zodiac Aerospace on large scale integration of electro-mechanical systems- Conducted bi-weekly teleconference meetings with American and French-based program management and engineering design teams to more effectively streamline solutions concerning schedule and technical obstacles- Active member of weekly customer focused presentations on requirement change requests and schedule updates- Responsible for creation of planning and test documents related to GCU hardware design and qualification based on company’s standard work processes- Created software package to validate GCU functionality in the presence of amplitude and frequency modulation

• Developed a software package to apply Abel Transforms to measure the axially symmetric distribution of soot formations• Created MatLab script files with image recognition capabilities to measure and detect volume changes during fluid washouts• Worked in tandem with PhD and Master’s Degree candidates on field measurement of soot volume fractions• Collaborated with Teaching Assistants in creating lectures for undergraduate and graduate level engineering courses