Philip Moy

Technical Skills

Programming Languages - Python, R, SQL, VBA, HTML, CSS
Machine Learning - Linear, Logistic, Ridge, Lasso Regression, Naive Bayes, Decision Trees, K-Means, KNN, PCA
Data Modules – Processing: dplyr, pandas, numpy. Machine Learning: scipy, scikit learn, caret, xgboost. Visualizations: ggplot2, shiny, matplotlib, seaborn
Software – Microsoft SQL Server, Oracle, PostgreSQL, Access, WebTrends

Professional Experience

Department of Finance - Data Analyst, 06/2016 - Present

• Collaborate with internal teams on project go lives to ensure payment data is being captured and consistent for external agencies.
• Analyze customer payment data to continuously improve policies that will benefit customers and decrease agency incurred costs.
• Developed and implemented automated ETL processes which feeds into unit-wide ad hoc reporting and trend analysis.
• Developed and maintained an interactive online dashboard and internal dashboard for agency outreach program which summarizes an agency’s current payment services and allows constructive feedback.
• Evaluated the design and ease of use of payment website by analyzing visitor’s platform usage to determine how users interacted with the website that assisted with the decision to redesign pages with heavy mobile usage.

New York City Data Science Academy - Data Scientist In-Training, 02/2015 - 05/2015

• Learned data processing techniques and machine learning algorithms for regression, classification, and clustering problems.
• Designed and developed a portfolio of analytical projects using structured and unstructured data.
• Collaborated with several multidisciplinary groups of my peers to participate in Kaggle data science competitions.

Grocery Haulers - Industrial Engineer, 03/2014 - 02/2015

• Utilized engineering techniques to implement new routes and improved routing solutions for current and future locations.
• Gathered data from various sources to build simulated scenarios that will estimate the cost basis for Request for Proposals.
• Performed data analysis to support the goal of finding long term efficiencies to reduce the overall cost of transportation service.

Walt Disney Company - Professional Intern, 08/2013 - 12/2013

• Identified the root causes of out of stocked inventory and corrected vendor records to prevent future delays with purchasing.
• Studied the workflow of operators and improved workflow efficiency by removing bottlenecks from the process.

Ryder Systems, Inc - Supply Chain Intern, 06/2013 - 08/2013

• Identified trends of loading/unloading times to drive continuous improvement activities that will increase efficiency of operations.

Independent Projects

Ames Housing Analysis - Applied regression models on housing data to accurately predict selling price of new homes.
eCommerce Item Clustering – Used text mining and clustering techniques on sales catalog data to determine best sales categories.
Scraping NBA Data – Web scraped NBA data to generate a local optimal lineup in daily fantasy sports.
Best Buy Reviews Analysis – Predicted the sentiment of customers based on their reviews to improve product feedback.
Lending Club Analysis – Trained classification models on loan data to predict status; resulting in higher investment returns.

Education

University at Buffalo, The State University of New York
Master of Science in Operations Research, 09/2013
Bachelor of Science in Industrial Engineering, 05/2012