Robert L. Underwood

Robert L. Underwood

Data Analyst



So either you stumbled upon this page or you were directed here. In any case, welcome to the home of Robert Underwood. Obviously, I’m Robert Underwood. Let me tell you a bit about myself.

I was born and raised in Pittsburgh, PA. As class valedictorian at Oliver High School in 2005, I was expected to do great things. Then I graduated with a Bachelor of Science in Mathematical Sciences from Carnegie Mellon University in 2009 into the worst recession seen since the Great Depression.

Not a promising start.

Since then, I’ve bounced around in various fields. In my career, I’ve been a desk attendant, test grader, appraisal and BPO reviewer, customer service representative, and bank teller. I went with being apathetic about politics to a conservative and from being unsaved to a born-again Christian. There have been various tips and tricks that I’ve picked up along the way, some of which I hope to share. Some of those were put to the test as I completed a Master of Science in Analytics from American University.

Now that I am officially a data analyst, I hope to improve my skills to the point where I am a data scientist. I’ve come a long way from my humble beginnings, but there is a long way to go

  • Data Science
  • Math
  • Statistics
  • Sports
  • MSc in Analytics, 2017

    American University

  • BSc in Mathematical Sciences, 2009

    Carnegie Mellon University


Microsoft Excel
Data Analysis
Data Modeling
Data Visualization including Power BI and Tableau


Data Analyst
Capgemini Government Solutions
Mar 2020 – Present Washington, DC
  • Monitors five mailboxes for various issues and logs cases in Access or Excel.
  • Optimized overdue case reports for clarity and brevity leading to closure of 97% of overdue cases.
  • Trains new employees on mailbox duties and case management.
Data Analyst
Aug 2019 – Mar 2020 Washington, DC
  • Analyzed complaints submitted through multiple sources, log summaries in Access, and forwards complaints to officers for follow-up action as needed.
  • Researched specific complaints using other internal databases and case logs.
  • Assisted in developing and testing new functions to increase productivity and reduce turnaround time.
  • Enforced new procedure for accurate data entry.
Junior Peoplesoft Developer
Fannie Mae
Jun 2018 – Feb 2019 Herndon, VA
  • Updated over 700 test scripts using Peoplesoft Test Framework.
  • Reviewed team interfaces and updated internal database with new interface control documents.
Data Analyst Trainee
FDM Group
Mar 2018 – May 2018 New York, NY
  • Received additional training in SQL including Oracle SQL and Microsoft SQL Server.
  • Trained and completed projects using SSIS and SSAS.
  • Completed a data visualization project using Power BI to create a sales dashboard.
Video Banking Teller
Dollar Bank
Sep 2015 – Mar 2018 Pittsburgh, PA
  • Serviced banking needs remotely from customers at 11 locations in three states.
  • Promoted machine functions via branch visits leading to a 75% increase in customer usage.
  • Built and maintained a new schedule template which reduced the time spent completing schedules by 50%.

Academic Projects

Email Marketing Data Group Project
  • Analyzed email marketing data – both company and third-party data to build a model that could predict email responses while reducing the total emails sent by over 50% and presented to company representatives.
  • Identified 15 variables out of 300 in the third-party dataset that resulted in a response rate outside of the average response rate and cleaned the data to create a new dataset for modeling.
  • Built, trained, and compared various models which were used to identify how many emails would need to be sent to retain 80% of responses.
Predictive Analytics Term Project
  • Identified and transformed variables in infant birth weight dataset in R to build models that could predict infant birth weight and provided suggestions.
AU Personal Care Group Project
  • Created a visual analytics tool in Tableau to identify key salespersons, customers, and products that were useful maximizing revenue and recommended the discontinuation of low-performing products
  • Summarized sales patterns and reported outlying high-performance month