Olney, Maryland, United States
Assistant language teacher with experience in leveraging data pipelines and analytics to make company dreams into reality. Troubleshoots problems and communicates solutions. Experienced Marine Science and Entomology researcher. Proficient in Python, COBOL, SQL and Data Modeling. Published Scientist catching bugs online and IRL.
- Automates workflows to improve government processes. - Maintains and provides periodic updates to government websites and other electronic resources. - Acts as liaison between the government and contractors in resolving unique technical challenges. - Prepares and provides publicly available documentation on how to access government resources. - Resolves customer issues in a timely and professional manner.
- Taught multiple sections of English as a foreign language to Japanese students in class sizes of approximately 40, enhancing their language skills and cultural understanding. - Assisted in grading assignments, quizzes, and tests, ensuring timely feedback and supporting student progress. - Created lesson plans and managed class time to ensure students meet MEXT objectives, fostering a structured and effective learning environment. - Communicated complex topics to a diverse audience from various cultural and language backgrounds, enhancing understanding and engagement. - Solved problems quickly and under pressure to keep lessons engaging and entertaining, maintaining student interest and participation.
- Analyzed over 200,000 data points residing within three MSSQL databases via the use of Python. - Leveraged Pandas to align matching columns with each database and create primary keys for analysis. - Ran cross reference searches and validations between databases primarily using Pandas and NumPy. - Stored and scheduled scripts by writing and running them inside Azure Databricks. - Built a Django-based web application which was able to connect to all three company database instances and link the information there in to provide real time displays. - Demonstrated the application to upper management. - Built both a Decision Tree, Random Forest, and K-Nearest Neighbors algorithms using the python packages Matplotlib, and scikit-learn to predict which train cars would be most likely to need repairs. - Leveraged scikit-learn and Pandas to isolate what portion of a train car would be most likely to need repair. - Achieved over 90% accuracy with at least two models.