Data Science Co-Op

at  Hunter Engineering

Overview:

Do you have a passion for data science/engineering? We’re hiring a Data Science Co-Op for our company’s headquarters in Bridgeton, Missouri. If you possess:

• Major study in Computer Science or Computer Engineering
• Experience with coding

Hunter could be a great fit for you! Family-owned since 1946, Hunter Engineering is the global industry leader in wheel alignment systems, wheel balancing systems, vehicle inspection systems, and more. Hunter equipment, made in America, is approved, and used by vehicle manufacturers, tire companies, and dealers in over 130 countries. Hunter was named a 2022 finalist for the Best Places to Work award by St. Louis Business Journal.
The Data Science Co-Op will be responsible for the research and development of various features based on Machine Learning. The Co-Op will work primarily on a Windows PC with Python while most of the machine learning training is done on a Linux machine. Our goal is to provide an experience that will be an important, effective first step in the career of a future engineer.
The Data Science Co-Op offers a set onsite schedule of 40 hours per week.
Co-Op Start Date: early January 2024
Co-Op End Date: mid-late August 2024

ADDITIONAL QUALIFICATIONS INCLUDE:

• Minimum 3.2 GPA (must be on resume)
• Major study in Computer Science or Computer Engineering
• Coding experience in C++, Python, or SQL
• Excellent communication skills

• Test and evaluate machine learning models.
• Data annotation and manipulation.
• Create and use PowerShell and Python scripts to acquire and organize data.
• Create and use VBA scripts in Excel as part of data evaluation.
• Create and use Python programs to clean and evaluate data.
• Work with source control tools like GIT to save and control data.
• Annotate data used for training an ML model. An example annotation program would be the use of Microsoft VoTT to identify and tag objects in images.
• Document frequently used processes for others to duplicate.
• Update training, test, and validation data used for a specific ML model.
• Work with an AI platform like TensorFlow or PyTorch.
• Configure and use a Linux PC to shorten ML model training times.
• Experiment with ML models and data to improve final results.
• Software design and testing.