Knight Aerospace: Data Analysis Intern

Students Plus Startups•San Antonio, TX

21h•Onsite

About The Position

Knight Aerospace, a global leader in advanced medical transport and modular aircraft solutions, is seeking a Data Analyst Intern to support our growing innovation and operations teams. This internship offers a hands -on opportunity to turn raw data into actionable insights that help advance aerospace engineering, manufacturing efficiency, and product innovation.

Requirements

Pursuing a degree in Data Science, Statistics, Computer Science, Engineering, Business Analytics, or related field.
Strong analytical and problem -solving skills, with attention to detail and data accuracy.
Experience with Python, R, or SQL; familiarity with data visualization tools (e.g., Tableau, Power BI) is a plus.
Understanding of machine learning techniques such as clustering, regression, and natural language processing.
Ability to communicate complex data insights clearly and effectively to diverse audiences.

Nice To Haves

Interest in the aerospace or advanced manufacturing industry is preferred.

Responsibilities

Analyze production, logistics, and operational data to identify performance trends, inefficiencies, and improvement opportunities.
Work with cross -functional teams—including engineering, supply chain, and R&D—to develop data models that support business decisions.
Use machine learning and data mining techniques to uncover patterns in large datasets, such as maintenance records or supply chain data.
Develop and test hypotheses to evaluate new strategies, projects, or technology implementations.
Utilize tools such as Python, R, and SQL to clean, process, and visualize complex datasets.
Create predictive or descriptive models (e.g., clustering, regression, or text analytics) to support forecasting and decision -making.
Prepare reports, dashboards, and visualizations to communicate key findings and present recommendations to technical and non -technical stakeholders.
Stay current with data science methodologies, tools, and best practices to improve modeling and analysis capabilities.