Work with stakeholders company wide to answer questions with our available data. Build custom dashboards to report KPIs and Customer Health. Work with the Customer Success team to create custom reporting for clients on a daily basis. Build frameworks and layout processes to transform data to meet the standard for Healthcare Registry ingestion. Cross collaborate with partnership companies to ensure data integrity and proper integration.

I recently completed a 4-month accredited Data Science and Machine Learning bootcamp through Metis. Metis focused on building the fundamentals of probability and statistics, Python, data analysis, data visualization, supervised and unsupervised machine learning, natural language processing, neural networks, database management, pipeline creation and model deployment. These skills were fostered through seven bi-weekly projects. Each project was constructed using real world data and the subject matter was completely up to the student. Below are a few of my projects I would like to highlight. Drowsiness Detection: • Built a real-time eye state image classification systems using a convolutional neural network (CNN) and computer vision. OpenCV was used to detect the face and eyes of a person in real time from a web camera. The area of the eyes were isolated and sent to the CNN model for classification. If both eyes were closed for an extended period of time a police siren was played. House Price Prediction:• Constructed a complete pipeline for data collection and deployment. House data was scraped from Realtor.com and stored in a SQL database. A pipeline was constructed to clean and train a linear regression model. A Streamlit app was constructed that will take real-time user input for house features and return a predicted house price. The application deployed on Heroku can be found (https://test-the-rainbow.herokuapp.com/).Putting a Price on Customer Churn: • Created a classification model that would assign soft probabilities to current customers based on their susceptibility to churn. With the use of a cost function and soft probabilities, a probability threshold was found that will optimize the amount of churn a company is willing to accept to maximize savings. A more in depth blog post can be found (https://medium.com/@stephen.blount99/putting-a-price-on-customer-churn-38a184e530b8).

• Responsible for constructing and analyzing complex structural analysis models using various structural programs and Excel.• Constructed 3D spatial models of buildings and produced comprehensive drawing sets for contractors to use as plans for construction of buildings.• Created complete calculation packages to document the entire design process for governmental review.

During my Master's degree I did a joint research project with the University of Nevada, Reno and the University of Auckland, New Zealand. I spent four months in New Zealand building four half scale concrete shear walls that were loaded with a pseudo static seismic response to test the overall repairability of the walls after seismic events. These walls all had innovative solution within the walls and were tested to see if these solutions effected repairability.

• Responsible for constructing four, half-scale concrete walls made with innovative materials to test the structures for earthquake susceptibility and repairability.• Processed and analyzed large datasets from 56 sensors on each wall using MATLAB.• Publication: “Evaluation of Design Modifications for Enhanced Repairability of Reinforced Concrete Walls,” Engineering Structures (2020) Vol. 206