E-Commerce Analytics Intern

Position Summary

Headquartered in Englewood Cliffs, N.J., Samsung Electronics America, Inc. (SEA), the U.S. Sales and Marketing subsidiary, is a leader in mobile technologies, consumer electronics, home appliances, enterprise solutions and networks systems. For more than four decades, Samsung has driven innovation, economic growth and workforce opportunity across the United States—investing over $100 billion and employing more than 20,000 people nationwide. By integrating our large portfolio of products, services and AI technology, we’re creating smarter, sustainable and more connected experiences that empower people to live better. SEA is a wholly owned subsidiary of Samsung Electronics Co., Ltd.
To learn more, visit Samsung.com. For the latest news, visit news.samsung.com/us.

Role and Responsibilities

Samsung Electronics America, Inc. is now accepting applications for its Summer 2026 Intern Program.  This exciting opportunity offers a paid, 10-week experience for selected senior or recently graduated students.  The program will run from June 1 to August 7. 

Eligible students will be seniors or recent graduates, possess U.S. work authorization, and be able to work full-time on a 5-day-per-week on-site schedule.  Additionally, participation in this program requires that you be located in the United States for the duration of the engagement.

Applications will be reviewed on a rolling basis, so early submission is encouraged. The application window is open until March 15, 2026; however, it may close earlier if all available roles are filled. Applications submitted after the application window or once a role is closed/projects are full will not be considered.

We are seeking a detail-oriented and analytical E-Commerce Analytics Intern to join our team in Plano, TX. As an intern, you will support the analysis of e-commerce data, identify trends, and provide insights to optimize online sales strategies and enhance customer experiences.

The E-commerce Analytics Intern will support the Analytics team in optimizing commercial performance through advanced statistical modeling. The primary focus of this role is to assist in the development and testing of models that analyze the correlation between pricing, service offerings, and conversion. You will work with diverse datasets to identify how variables, such as delivery speed and warranty options, influence the conversion. This position is ideal for a student looking to apply machine learning and statistical theory to high-impact, full-funnel ecommerce business solutions.

Key Responsibilities

• Assist in the collection, cleaning, and analysis of e-commerce data to identify trends and patterns.
• Develop and maintain dashboards and reports to track key performance indicators (KPIs) for online sales.
• Analyze customer behavior and purchase patterns to inform marketing and sales strategies.
• Collaborate with cross-functional teams to support data-driven decision-making for e-commerce initiatives.
• Conduct A/B testing and analyze results to optimize website performance and user experience.
• Monitor and analyze competitor e-commerce activities to identify opportunities for improvement.
• Prepare presentations and reports to communicate findings and recommendations to stakeholders.

Skills and Qualifications

• Currently pursuing a bachelor’s degree in Data Science, Business Analytics, Statistics, or Computer Science.
• Basic understanding of statistics and data analysis methods.
• Strong analytical and problem-solving skills.
• Excellent communication and teamwork abilities.
• Eagerness to learn and adapt in a fast-paced environment.
• Ability to generate data visualizations to communicate insights to team members.

Technical Skills Required:

• Programming: Python (Must-have) and R (Desirable).
• Database & Spreadsheet: SQL and Excel.
• Modeling & Analysis: Logistic regression models; experience with data cleaning, preprocessing, and exploratory data analysis (EDA).
• Version Control: Fundamental knowledge of Git.
• Domain Focus: Understanding of mathematical modeling for pricing sensitivity, service attachment rates, and supply chain lead times.

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