The Data Analyst Intern supports the Human-in-the-Loop (HITL) service pod by assisting with data processing, quality checks, and the collection of academic course catalog information. The intern helps maintain high data accuracy standards while learning key concepts in analytics, data quality, and AI-assisted workflows.

Responsibilities

• Assist with reviewing AI-generated transcript extractions and flagging errors for correction.
• Support the cleaning, formatting, and organization of datasets used for TCE processes and PROSPECT Course Co-Pilot.
• Help gather and input course catalog information using structured templates.
• Conduct basic reporting on accuracy rates and processing progress.
• Perform simple SQL queries or spreadsheet analyses under supervision.
• Help identify minor data issues and propose improvements.
• Document steps, findings, and improvements for training datasets.

Essential Functions

• Perform data entry and validation tasks with high attention to detail.
• Review computer reports and indicators to identify quality issues.
• Maintain well-organized data files, documentation, and updates.
• Collaborate with team members to ensure timely completion of data tasks.

Minimal Qualifications

• Currently studying Mathematics, Statistics, Engineering, Economics, Computer Science, Information Systems, or a related field.
• Basic understanding of data concepts such as datasets, tables, and data cleaning.
• Comfortable working in Excel or Google Sheets.
• Strong attention to detail and willingness to learn technical processes.
• Ability to follow structured workflows and accuracy requirements.

Preferred Qualifications

• Introductory experience with SQL or any statistical tool is a plus.
• Familiarity with academic environments or higher-education data is desirable.
• Interest in AI, machine learning, or data quality processes.

Top Competencies for Success in This Role

• Learning Agility – Quickly absorbs new tools and academic data structures.
• Attention to Detail – Ensures high-quality data handling.
• Analytical Thinking – Applies logic and curiosity to identify data issues.
• Collaboration – Works well within a fast-paced service pod.
• Ownership & Reliability – Delivers accurate results and meets deadlines.