I am a curious, solution-oriented, software developer with a passion for building clean, maintainable, and human-centered applications. After studying Computer Science at the University of Georgia, I joined Griffin & Strong where I engineered data analysis pipelines for four governments. I currently work at Guidehouse where I support a variety of agile consulting teams through development of interactive dashboards, data models, ETL pipelines, and automated workflows. I am eager to contribute to a team where I can leverage my data engineering and consulting experience to support full stack development.
|Guidehouse (formerly PwC Public Sector)||
|Solutions Consultant, Advanced Analytics and Intelligent Automation||
Jan. 2021 to Current
Developed automation, ETL, and feature engineering processes for exploratory, descriptive, and predictive risk-based analytics for the Center for Medicare and Medicaid's payment models
Collaborated on front-end development of fraud detection MVP with Vue, Quasar, and OpenLayers, improving SaaS capabilities within firm
Created data validation algorithm to isolate errors in user-entered health provider data using Pandas
Delivered three data models and data entry/validation applications for auditing and reporting which were reused on multiple auditing teams
Created exploratory visualizations and drill-down dashboards with Altair, Plotly, and Power BI
Communicated with stakeholders to advise data and automation strategy with respect to infrastructure, timeline, and budget
Empowered business users by creating documented self-service "Stored Processes" and a Community of Practice, reducing reliance on IT
|Griffin & Strong P.C.||
May 2020 to Jan. 2021
- Completed data collection, preparation, validation, and statistical testing of disparity index for City of Chattanooga, City of Frederick, Mecklenburg County, and Cuyahoga County which serve as a factual predicate for business inclusion programs
- Completed utilization analysis for Invest Atlanta and Clayton County
- Lead effort to create supervised classifier to solve time consuming and costly data labeling problems
- Created DAGs for reproducible transformations of data
- Standardized operating practices for efficient and validated research
- Mentored data analyst to improve their analysis process and follow best practices
Nov. 2018 to May 2020
- Translated research methodology from subject matter experts into Python and adapted algorithms to each organization's constraints
- Performed exploratory analysis on collected data from governmental organizations to test data quality and give recommendations to aid future studies
- Isolated data gaps and anomalies and cleaned data for reproducible analysis
- Communicated with external teams and stakeholders to collect data and present findings
|Lean Six Sigma · Yellow Belt||
|University of Georgia||
|Student Data Management||
A live web application for creating, updating, and deleting records related to students built with Spring, Java, Maven, React, and deployed to AWS through a CI/CD pipeline.
An open source Python library containing pre and post-processing algorithms for wrangling common problem patterns in Excel files. The primary problem solved is resolving entities in joined data systems and traceability of rule based decisions.
An analysis of NYC public schools which provides a recommendation to PASSNYC for how they should distribute their services to improve the diversity of the specialized high schools
A Python software allowing non-programmers to take advantage of the open-source "pandas-profiling" library which creates comprehensive HTML summaries of raw data files. This summary includes data warnings, missing values, and correlations. The front-end was built using PyQt and can run on any operating system.