The AI Engineer Intern is a learning-focused role designed for currently enrolled college students pursuing a degree in Information Technology, AI, GenAI, Data Science, Computer Science, Statistics, or a related field. The intern supports AI initiatives by assisting with data preparation, exploratory analysis, documentation, and basic modeling and prompting tasks under the close guidance of junior and senior Engineers and Data Scientists. This role emphasizes hands-on exposure, skill development, and real-world application of academic concepts, while contributing to team projects in a structured and supervised environment.

This will be a 10–12-week, summer internship located at: Sherwin-Williams Global Headquarters: 1 Sherwin Way, Cleveland, OH 44113. This internship will be approximately 37.5 hours per week.

Job duties include contact with other employees and access confidential and proprietary information and/or other items of value, and such access may be supervised or unsupervised. The Company therefore has determined that a review of criminal history is necessary to protect the business and its operations and reputation and is necessary to protect the safety of the Company’s staff, employees, and business relationships.

Minimum Requirements:

• Currently enrolled in a Bachelor’s or Associate’s Degree program in Data Science, Statistics, AI, Computer Science, Information Technology, Business Analytics, or related field

• Coursework or project experience using SQL

• Familiarity with AI tools (e.g., Microsoft Copilot, Claude Code)

• Exposure to Machine Learning concepts through classes or projects

• Experience using version control (e.g., Git)

Preferred Qualifications:

• Coursework in Statistics, Data Analysis, Programming, or Machine Learning

• Participation in academic projects, hackathons, or analytics competitions

• Assist with collecting, cleaning, and organizing datasets used for analysis and modeling.

• Support exploratory data analysis to identify trends, patterns, and data quality issues.

• Help prepare visualizations, summaries, and basic reports to communicate findings.

• Shadow and support Data Scientists during model development, experimentation, and validation activities.

• Document analytical processes, assumptions, and findings for internal use.

• Learn foundational concepts in AI, GenAI, Statistics, Machine Learning, and Data Science workflows.

• Participate in team meetings, reviews, and learning sessions to build technical and business knowledge.