Mack McGlenn

Data Scientist

Mack McGlenn


Data Scientist


(980) 318-9771

Available Work Locations: Charlotte, NC • Columbia, SC • NC • Remote • Washington D.C.

Military Veteran

Inactive Secret

Some college

Core Technologies: Python, MySQL, Spark, Tableau, Pandas, NumPy, Matplotlib, Seaborn, SciKitLearn, Anaconda, Jupyter Notebooks, Git/GitHub

Core Competencies: Data Storytelling, Applied Statistics, Machine Learning, Natural Language Processing, Classification, Regression, Clustering, Time Series Analysis, Anomaly Detection

Primary Focus: Data Analytics

Additional Skills: Microsoft SQL • Python • Tableau

Hire Me Because

You’re looking to add someone to a team whose diverse life experiences and attention to detail makes them approach problems with new, unexplored insights. That’s me. I have the ability to self-manage my workload when given a reasonable time frame, but I also appreciate the value in collaborating as a member of a team towards a common goal. My time at Codeup has reinforced my previous technical proficiency while affording me hands-on experience with new skills. I don’t know everything, but I possess a willingness to never stop learning and the understanding to retain what I learn. I’m passionate about understanding, discovering patterns, and improving processes, and I’m self-aware enough to know when to seek out the advice and mentorship of more experienced people in the field. All of those reasons and more are why I’ll make an excellent addition to your team.

My Capstone Project: The Fairness of Fair Market Rent: An Analysis of HUD Fair Market Rent and Availability of Choice in Housing Choice Vouchers

This project analyzes whether the fair market rent rates set by the U.S. Department of Housing and Urban Development (HUD) keep pace with actual market rental rates. HUD uses Fair Market Rents to establish the maximum housing assistance voucher amount a household may qualify for. Any additional expenses are paid out of pocket by the household receiving the voucher. If FMRs are calculated too low, it becomes more difficult for voucher users to acquire adequate housing. Using a combination of time-series analysis and modeling, the findings suggest inconsistencies in variance between actual market rent and FMR rates. Using a Holt’s Seasonal Trend algorithm, we trained our model to outperform our baseline by 48%, reducing the margin of error by from $155 RMSE to $80 RMSE.

“With technology, I am most passionate about growing on my foundational skills and practicing efficiency.