Nathan E. Fogal
Washington, DC 20007
***********@*****.*** 585-***-****
EDUCATION
University of Virginia, Data Science Institute, Charlottesville, VA May 2017 Master of Science in Data Science, GPA: 3.9
Relevant courses: Data Ethics, Data Mining, Data Visualization, GIS, Machine Learning, Statistical Consulting University of Virginia, College of Arts & Sciences, Charlottesville, VA May 2016 Bachelor of Arts in Statistics (Actuarial Finance Concentration), Three-Year Graduate Relevant courses: Linear Models, Machine Learning, Nonparametric Statistics, Sample Surveys, Time Series WORK EXPERIENCE
Accenture, Washington, DC July 2017 – Present
Analytics and Technology Analyst
• Facilitate data-driven decision making and risk management for a federal client through delivery of analytical insight
• Utilize machine learning and statistical modeling to develop proactive fraud models using Python, R, and SAS
• Analyze millions of unstructured transactions using text analytics and data mining for major fraud investigations
• Lead data quality improvement initiatives and strengthen agency budget optimization using time series forecasting
• Drive increased efficiency by automating client-facing reports and producing interactive Tableau data visualizations
• Build enhanced databases in SAP HANA to expand the analytical capabilities of various agency organizations
• Develop and monitor real-time interactive business intelligence dashboards for international clients
• Provide back-end software improvement and code migration support for logistics management systems Critical Incident Analysis Group, Charlottesville, VA August 2013 – May 2017 Program Coordinator Intern
• Led a student team that delivered analytical support to an agency seeking strengthened intergovernmental cooperation
• Communicated complex statistical findings to aid lawyers and doctors in significant court cases
• Built and analyzed datasets of sensitive records using R to reinforce litigation responses for the Virginia DOC
• Orchestrated multiple 50-participant conferences on topics of interest among international law enforcement agencies
• Managed a $50,000 budget and reported to executives regarding financial stability and growth of the organization General Faculty Council, University of Virginia, Charlottesville, VA September 2015 – May 2017 Data Modeling Intern
• Cleaned and analyzed sensitive demographic data using R to increase transparency of faculty government structure
• Utilized Tableau to create visualizations of critical statistical findings for use in board meetings
• Presented recommendations to promote increased diversity among faculty leadership to university officials SELECTED PROJECTS
Adversarial Learning in Credit Card Fraud Detection (Capstone) August 2016 – May 2017
• Collaborated with a major financial institution to develop a novel approach to credit card fraud detection
• Produced defensive and offensive models to clean and analyze 80 million real transactions using Python, R, and AWS
• Constructed adaptive algorithms to instantaneously detect fraudulent transactions prior to approval
• Forecasted evolving fraudulent strategies using game theory to preemptively reinforce the detection models
• Published a technical research paper that won the Best Paper Award at the IEEE SEIDS Conference 2017 Optimizing Uber May 2017
• Developed algorithms using Linear Regression and Gradient Boosted Trees to optimize Uber prices against competitors Understanding Police Shootings February 2017
• Used Random Forests, Association Rule Mining, and BBN’s to determine the leading causes of police shootings globally Sentiment Analysis of the 2016 Presidential Election October 2016
• Applied sentiment analysis to scrapped tweets from Twitter to develop accurate election polls and favorability ratings SKILLS
Technical: Business Intelligence, Data Mining, Data Visualization, Machine Learning, Statistical Modeling, Time Series Programming: Python, R, SAS, SQL, Tableau, Java, ArcGIS, AWS, HTML, JavaScript, SAP HANA Certifications: ICAgile Certified Professional