- Complete assembly processes with minimal defects below set takt time, often performing multiple processes- Work with other associates and production supervisor to improve production rate and quality, reduce costs- Communicate with maintenance and engineers to quickly resolve issues with machinery, tooling, or processes- Met or exceeded production targets despite massive production ramp in 2019 and global pandemic in 2020

-Cleaned, wrangled, and analyzed datasets to uncover actionable insights-Developed job-ready skills in SQL and Python using PostgreSQL and Google Colaboratory-Wrote Python code in line with PEP-8 guidelines, with clear documentation and commentary-Created presentations designed for a general audience to communicate my findings-Developed models for linear/logistic regression, random forest, KNN, k-means, hierarchical clustering, and DBSCANCapstone Projects:A/B Testing | Compared 2010, 2014, and 2018 commuting data from American Community SurveyAnalyzed the statistical significance of changes in commute time, vehicle occupancy, and means of transportBuilt with: python and pandas, numpy, scipy, and seaborn libraries in Google ColaboratoryRegression | Developed a model to predict fuel economy from EPA vehicle datasetCleaned, analyzed, and visualized data to select features for regression (year, class, transmission and fuel type)Used ordinary least squares (OLS) and random forest regression techniques to predict fuel economyBuilt with: python with pandas, numpy, scikit-learn, sqlalchemy, and matplotlib libraries in Google ColaboratoryClustering | Developed a model for clustering charging station customers into usage segmentsUsed unsupervised learning (k-means, hierarchical clustering, DBSCAN) to cluster charging transaction dataUsed dimensionality reduction techniques (PCA, TSNE, UMAP) to visualize features and clustering resultsBuilt with: python with pandas in Google ColaboratoryNeural Networks | Developed a Long Short-Term memory network for state of charge estimationUsed voltage, temperature, and current data to predict future state of charge for lithium-ion batteryBuilt with: python with pandas in Google Colaboratory, MATLAB for converting files from .mat to .csv format

- Managed $400,000 Revolving Loan Fund for campus sustainability projects- Reviewed student grant applications and awarded nearly $50,000 in project and research grants- Developed and defended $389,000 annual budget for Student Incidental Fees Committee

Devised an experiment to study the effect of UV radiation on the degradation of the chemical structure of polystyrene thin films over time.Measured and prepared chemical solutions, carefully cut mono-crystalline silicon wafers into squares. Chemically washed and sterilized each sample.Reviewed literature and conducted experiments to determine the proper spin-coating parameters to produce 100-nm thin films.Used spin-coater to produce thin films, used ellipsometer to determine the thickness of each film (target = 100 nm), used UV-ozone cleaner to oxidize the samples, and characterized the chemical structure of the samples with X-ray photoelectron spectroscopy.