Student Hourly Assistant - Data Annotation & Visualization Support

Texas A&M University System • Prairie View, TX

1d • $13 • Onsite

About The Position

We are seeking a highly motivated and detail-oriented undergraduate student to support a data-driven architecture and engineering research project. The successful candidate will assist with data annotation, visualization, and basic Python scripting to support undergraduate learning and research workflows.

Requirements

• Presently enrolled at Prairie View A&M University for at least nine (9) graduate or six (6) undergraduate semester credit hours during the term in which the work is to be done.
• Must be in good academic standing (SAP) as demonstrated through your college; minimum 2.0 GPA
• Sophomore level or above
• No prior experience necessary
• For work eligibility during a summer term, a student must be enrolled for at least three (3) graduate or undergraduate semester credit hours during the term in which the work is to be done or preregistered at least six (6) undergraduate semester hours or nine (9) graduate semester hours for the upcoming fall term.

Nice To Haves

• Enrolled in Computer Science, Engineering, or related fields
• Intermediate proficiency in Python, especially for data handling and visualization.
• Familiarity with Jupyter Notebooks, Pandas, and data wrangling tools.
• Attention to detail and consistency in data processing tasks.
• Excellent communication and collaboration skills.
• Familiarity with machine learning or large language models (LLMs).
• Prior experience in training or evaluating AI models.
• Exposure to AI in AEC, geospatial data, or environmental analytics.

Responsibilities

• Assist with annotating datasets related to the built environment and smart building applications.
• Develop simple Python scripts to help undergraduate students visualize data sets (e.g., using Matplotlib, Seaborn, or Plotly).
• Create clear, well-documented code templates for generating graphs, charts, and basic dashboards.
• Support faculty and students in formatting and preparing data for use in workshops and class modules.
• Collaborate with the research team to track and refine labeling and annotation standards.

What This Job Offers

• Job Type: Part-time
• Career Level: Entry Level
• Education Level: No Education Listed