Data Analytics Internship (Retail Performance)

Samsung Electronics • Plano, TX

1d • Onsite

About The Position

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 UNDERGRAD OR GRAD (based on HM request) 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. 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 Position Summary This internship opportunity is with the Retail Performance Analytics team, which focuses on evaluating and improving store tiering, Go-to-Market (GTM) strategy, and overall retail performance through data-driven insights. As an intern, you will support tiering post-mortem evaluations by analyzing historical performance, designing and executing A/B testing, and applying classification and machine learning techniques to refine tiering methodologies. You will collaborate with analytics and business stakeholders to translate model outputs into actionable recommendations that enhance store coverage, training effectiveness, and sales performance.

Requirements

• Currently pursuing a Master's Degree.
• Preferred academic fields of study: Data Science, Statistics, Computer Science, Analytics, or a related field.
• Final-year students or recent graduates are strongly preferred.
• Currently pursuing a degree in Data Science, Statistics, Computer Science, Analytics, or a related field.
• Strong analytical and problem-solving skills.
• Good communication skills and the ability to work in a cross-functional team environment.
• Experience with data analysis and statistical testing (A/B testing, hypothesis testing).
• Machine learning fundamentals, especially classification models (e.g., logistic regression, random forest, gradient boosting).
• Data processing and modeling using Python or similar tools.
• Ability to visualize and present insights clearly using tools like Excel, PowerPoint, or Tableau.

Nice To Haves

• Basic understanding of retail, sales performance, or GTM concepts is a plus.

Responsibilities

• Conduct in-depth analysis of historical retail performance data to identify trends and insights.
• Design and execute A/B testing experiments to evaluate the effectiveness of store tiering strategies.
• Apply classification and machine learning techniques to refine and improve tiering methodologies.
• Collaborate with cross-functional teams to translate analytical findings into actionable business recommendations.
• Develop and present data visualizations and reports using tools like Excel, PowerPoint, or Tableau.
• Support the evaluation of Go-to-Market (GTM) strategies by analyzing their impact on retail performance.
• Assist in the development of predictive models to forecast store performance and optimize resource allocation.
• Contribute to the documentation of analytical processes and methodologies for future reference.

What This Job Offers

• Job Type: Full-time
• Career Level: Intern