Developed an interactive curriculum on topics such as python programming (i.e., Pandas, Scikit Learn, Jupyter notebooks, GitHub), data fundamentals (EDA, supervised and unsupervised learning), and advanced topics in machine learning/AI (NLP, Neural Networks, Time Series) for hundreds of students across the United States including the Louisville Reskills Data Academy. Mentored students with strategies to breakdown complex problems for stakeholders into actionable, digestible steps and collaboratively create roadmaps to achieve project goals helping them complete successful AI/ML projects. Authored a lesson on new Sci-Kit Learn methods including advanced pipelines and column transformers and one on spaCy NLP techniques and trained NLP pipeline deployment. Successfully delivered weekly lessons on Recurrent Neural Networks and Long Short-Term Memory for times series modeling in keras, pandas for data analysis, word embeddings with gensim and nltk, data ethics, and ensemble methods.

Remotely mentored 30 students from across the U.S. through individualized projects utilizing Excel, SQL, and Tableau to extract and visualize insights from large data sets while maintaining the primacy of ethical and responsible data stewardship.

Develop an interactive curriculum on topics such as python programming (i.e., Pandas, Scikit Learn, Jupyter notebooks), data fundamentals (EDA, predictive modeling), and advanced topics in machine learning/AI (NLP, CNN, Time Series) for industry leaders in sectors ranging from finance (Fannie Mae), the auto industry (Ford), tech (Inuit) and healthcare (Humana).Model and explain strategies to breakdown complex problems for clients into actionable, digestible steps and collaboratively create roadmaps to achieve company project goals helping clients complete successful AI/ML projects.

Two consecutive 10-week programs designed to train current employees at a major financial institution who are interested in learning data science so that they can add to their skills and join Fannie Mae's growing team of data scientists. The curriculum is a condensed version of the Data Science Immersive program which covers various machine learning and deep learning techniques.- Helping to manage and monitor the progress of a class of adult students, usually about 20 per cohort.- Grading and providing feedback for the bulk of the coursework, which includes 4 projects.- Assisting students with any questions they might have during the course. This includes both one-on-one instruction as well as teaching to the group when necessary.

Completed full-time immersive Data Science program that included over 500 classroom hours of professional training in 12 weeks. Utilized a hands-on approach that focused on real-world application of NLP, Linear and Classification Modeling, and Python. Developed a portfolio of individually and collaboratively focused in-class projects.

Oversaw operations of luxury, client-focused barbershops in three locations across DC. Served as a liaison between clients and barbers, resolved conflicts between barbers, clients, and management. Scheduled, managed, and maintained a database of over 200 clients per day and over 600 clients per week.

Reviewed and edited approximately 30 technical reports per week. Met with engineers to discuss logical fallacies and other logistical writing errors. Demonstrated patience and understanding with ESL engineers as evidenced by engineer feedback. Accustomed to handling sensitive, confidential forensic legal documents.

Created lesson plans that underscored the primacy of critical thinking in the curriculum. Collected and commented on over 40 student papers a week for 14 weeks a semester. Student won “Best Student Paper” Grant.