Data Scientist/Econometrician
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M.S in Applied Economics
Two Nil Holdings – Data Scientist, Los Angeles CA: Apr. 2022–Present
▪ Development of software and programs utilizing custom SQL queries and cutting-edge Python packages for Data Science such as pandas, numpy, scikit-learn, and scipy,
▪ Oversaw the reconstruction of Mixed Marketing Models for improved forecasting accuracy, numerical optimization, and efficiency of investment strategies for marketing and advertising campaigns, resulting in an approximately
▪ 94% reduction in model runtime,
▪ Applying K-Nearest Neighbors (KNN) algorithms in Python for tasks such as data classification and optimization for strategy planning and decision-making data science products,
▪ Demonstrating thorough, clear understanding of client data elements, domains, and the inter-dependencies between them through engaging and conversation product presentations with clients from many different sectors of business and levels of seniority,
▪ Undertaking a full-scale Digital Pathway Analysis product utilizing Amazon RedShift and Python for a Business-to-Consumer (B2C) website, including User Journey Analysis, thorough statistical analysis of marketing campaigns’ success rate amongst a random sample of digital marketing/pixel data, and customer segmentation reporting.
EWOP Studios, LLC. – Data Engineer, Seattle, WA: 2020–Present
▪ Development of Univariate time-series forecasting model framework software and programs for estimating clients’ Digital Streaming performance utilizing scikit-learn and statsmodels in Python,
▪ Univariate model framework programs on average yield a forecasting accuracy of approx. 93% across all Digital Streaming platforms,
▪ Development of multivariate time-series forecasting model framework software and programs for estimating clients’ Digital Streaming and Social Media Platform engagement utilizing scikit-learn and statsmodels libraries in Python,
▪ Multivariate model framework programs on average yield a forecasting accuracy of approx. 93% across all Digital Streaming and Social Media platforms,
▪ Developing and implementing Machine Learning approaches and programs in Python, such as Long-Short Term Memory (LSTM) Recurrent Neural Networks (RNN), utilizing high-level, industry-standard Data Science libraries such as PySpark TensorFlow, and Keras
Washington State University – Research Assistant, Pullman, WA: 2020–2021
Thesis for M.Sc. in Applied Economics:
Analyzing the Effects of the U.S–China Trade War on Apparel and Textile Supply Chains, 2021.
▪ Worked closely under my Committee Chair Dr. H. Alan Love, Ph.D., Director and Professor at Washington State University School of Economics, and faculty members of varying disciplines,
▪ Novel dataset constructed completely utilizing the Python Programming Language, containing approximately 40,000 total observations
▪ Developed econometric models in Python for estimating the effects of negative supply chain ‘shocks’ brought about by U.S.–China trade negotiations of recent years on all stages of textile/apparel supply chains for bilateral trade between multiple different countries using the linearmodels, statsmodels, and scikit-learn Python libraries,
▪ Our model’s estimation showed that bilateral trade between certain countries, in the periods of time leading up to and during the U.S.–China Trade War of recent years, reflected the presence of a bullwhip effect in apparel and textile supply chains.
