Data Analyst and Coordinator Intern

Role Overview

This position sits at the intersection of data, operations, and strategy. On a typical day, you will pull together information from multiple internal systems, clean and validate it, then turn it into clear reports and dashboards that decision-makers actually use. Your analyses will directly shape how the company plans budgets, tracks performance, and improves day-to-day efficiency, so the accuracy and clarity of your work matter across every department.

Key Responsibilities

• Interpret complex datasets using statistical techniques and analytics tools, translating raw numbers into practical recommendations for leadership.
• Gather, process, and combine data from various sources, then perform rigorous checks to ensure the information is complete, accurate, and consistent.
• Build and maintain interactive dashboards and visual reports that track key performance indicators and make trends easy to understand at a glance.
• Present findings to both technical and non-technical audiences, adapting your communication style so that every stakeholder walks away with a clear call to action.
• Support fiscal management tasks by preparing departmental statistics, monitoring spending patterns, and highlighting anomalies that require attention.
• Apply business intelligence tools to identify correlations and predictive signals that help the company anticipate future performance and avoid operational bottlenecks.
• Follow all approved standard operating procedures and quality management policies, ensuring that every data output complies with internal governance and data privacy standards.

Requirements & Qualifications

• Bachelor’s degree in data science, statistics, computer science, or a closely related field.
• At least one year of hands-on experience as a data analyst, business analyst, or in an equivalent role where you owned reporting and analysis end to end.
• Strong command of data visualization platforms such as Tableau, Power BI, or similar tools, with a portfolio that shows you can turn raw data into intuitive visuals.
• Working knowledge of SQL, Python, or R for querying and manipulating data; practical experience with at least one of these is strongly preferred.
• Solid grasp of statistical methods, hypothesis testing, and predictive modeling concepts, with the ability to choose the right approach for each business question.
• Familiarity with machine learning frameworks and how they can be applied to real-world operational problems is a plus.
• Understanding of data governance principles, data privacy regulations, and the importance of maintaining confidentiality when handling sensitive company information.