MPS Group - Data Analyst

Facility Services Group

Pioneered the implementation of PowerBI into their Alpha Program, which is utilized to manage and execute contracts and service delivery daily using real-time tracking software.

Worked closely with regional managers to maintain data accuracy, ensuring high-quality, reliable information across all regions. Collaborated on enhancing the quality of data visualizations, resulting in more effective and insightful reporting that supported strategic decision-making.

Developed strong presentation skills through collaboration with the regional managers and significantly advanced my data visualization / cleaning abilities using both Power BI and Python.

Michigan State University - Graduate Teaching Assistant

Department of CMSE

Collaborated with the professor to design a graduate course that introduces non-technical students to advanced programming methods within python.

Responsible for more than 80 students understanding of python programming methods as well as assisting them with creating and implementing computational methods for their thesis through a pythonic approach.

Massively improved my ability to translate complex technical skills to non-technical students from an array of differing fields of study by working one-on-one with each student during and outside of class.

MPS Group - Data Science Intern

Technical Services Center

Worked in the environmental field, spearheading the implementation of a web-based application that MPS can use on-site at over 100 of their partner locations.

Used Microsoft BI and PowerApps to create the application using well over 200 different data sets, each one being site specific.

Enhanced interpersonal communication and self-directed learning skills by mastering new tools and technologies, demonstrating adaptability and a commitment to continuous improvement.

Undergraduate Research Assistant

Michigan State University College of Engineering

Assisted a Ph.D. student with the development of both an analytical and machine learning approach to identifying and tracking topological defects using Python.

Used open-source software in Linux to conduct post-processing and visualization of large datasets. Utilized high performance computing resources to run simulations in parallel via Michigan State University's Institute of Cyber Enabled Research (ICER).

Responsible for a magnitude of high-level math and coding projects with stringent timelines that helped improve my ability and speed of learning.

Undergraduate Learning Assistant

Michigan State University College of Natural Science

Collaborated with other ULA’s and the professor to design engaging labs and lectures for students.

Tutored groups of students with any homework or test-prep questions. (Groups of 10+ students).

Gained interpersonal communication skills, improved my ability to communicate complex technical material to non-technical audiences by teaching non-STEM students from diverse knowledge backgrounds.

Undergraduate Research Assistant

Facility for Rare Isotope Beams

Collaborated with a team of researchers to design and fabricate the first resistive parallel plate avalanche chamber (PPAC) in order to produce a more stable image of decay for a Hadron particle.

Rapidly taught myself how to use Autodesk’s Eagle program to design a printed circuit board (PCB) that was implemented within the resistive PPAC.

Emerged as a leader, and collaborator, within a group of more than 15 researchers, overseeing presentations highlighting updates on development and noting next steps to be taken within the lab.

Undergraduate Research Assistant

Michigan State University College of Education

Assisted a group of professors in studying the effects of a new form of science instruction modeled after the new “Next Generation Science Standards.”

Responsible for large-data analysis and visualizations within Python to support claims of the new form of science instruction being more beneficial to students (K-12).

Gained the understanding of how to properly use data to support claims, both visually and statistically, using Python, all without losing any information/data throughout the visualizing process.