7-Eleven is hiring for a Data Science Intern role in US

Position:

• Data Science Intern

Company:

• 7-Eleven, Inc.

Location:

• Irving, Texas, USA

Job type:

• Internship

Job mode:

• On-site

Job requisition id:

• R25_0000003903

Years of experience:

• Not specified; open to current students or recent graduates

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Company Description:

• 7-Eleven is a globally recognized name in the convenience retail sector, pioneering the modern convenience store model.

• Established as the original innovator in this industry, 7-Eleven continues to lead with groundbreaking ideas and practices in retail.

• With a portfolio of over 84,000 stores worldwide, 7-Eleven is considered the largest operator, franchisor, and licensor of convenience stores globally.

• The company has consistently pushed the envelope in introducing new concepts, such as being the first in the U.S. to offer fresh-brewed, take-away coffee.

• Their iconic Slurpee® has become a cultural phenomenon with more than six million consumed annually since its inception in 1966.

• Demonstrating a deep commitment to innovation, 7-Eleven rolls out over 2,500 new products each year to meet evolving customer preferences.

• The organization is also one of the largest independent gasoline retailers in the United States.

• It has earned numerous awards and accolades for excellence in franchising and operational efficiency.

• The mission of 7-Eleven remains clear—to make life easier for its customers by offering products and services whenever and wherever they are needed.

• Their work culture emphasizes innovation, customer-centricity, and the use of data-driven approaches to achieve superior business results.

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Profile Overview:

• The Data Science Internship at 7-Eleven is a structured, paid summer program designed to give participants real-world exposure to the field of data science.

• Lasting between 10 to 12 weeks, the internship is set during the summer and allows interns to specialize in a specific functional area within the company.

• Interns will engage in meaningful projects, exploring real datasets and applying modern data science techniques under professional mentorship.

• The experience will culminate in a capstone project, which will be presented to key functional leaders at the end of the program.

• A designated mentor or coach will work closely with each intern, offering regular check-ins, guidance, and constructive feedback throughout the duration of the internship.

• Participants will take part in a lunch-and-learn series, providing insights into various departments and opportunities to connect with senior leadership.

• The program also includes store visits, where interns can experience firsthand how 7-Eleven operates at the retail level.

• Interns will explore the company’s distribution channels and learn about how data is used to streamline business operations and customer satisfaction.

• They will participate in events such as Hack Day, and be involved in data science-related initiatives that directly support company goals.

• This internship is ideal for those who are enthusiastic about data, eager to learn, and passionate about contributing to a forward-thinking business environment.

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Qualifications:

• Candidates should be currently enrolled in, or have recently completed, a Bachelor’s or Master’s degree program in a relevant field such as Data Science, Computer Science, Statistics, Mathematics, or similar.

• Strong foundation in programming, especially in languages commonly used in data science like Python or R.

• Hands-on familiarity with libraries used for data manipulation such as Pandas or similar.

• Awareness of machine learning models, including both supervised and unsupervised learning techniques.

• Proficiency in data visualization tools or libraries such as PowerBI, matplotlib, or seaborn.

• Basic understanding of SQL and relational databases, including the ability to write simple queries to extract data.

• Solid grasp of statistical concepts such as distributions, p-values, confidence intervals, and hypothesis testing.

• Excellent analytical and problem-solving skills, with an eye for detail and data quality.

• Ability to work in a team-oriented environment, taking initiative and responsibility for assigned tasks.

• Effective communication skills for presenting data-driven insights to both technical and non-technical audiences.

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Additional Info:

• Interns will receive a comprehensive, immersive experience, allowing them to apply classroom learning in a real business context.

• They will get hands-on exposure to challenges in retail analytics, customer segmentation, inventory optimization, and demand forecasting.

• The internship is highly collaborative, offering a chance to work directly with data scientists, engineers, and business strategists.

• Interns will receive mentorship from experienced professionals who will provide feedback and career advice.

• The program features structured learning modules to keep interns aligned with the latest trends and tools in data science.

• Projects undertaken during the internship may impact important company decisions, offering meaningful contributions beyond just academic learning.

• Interns are expected to demonstrate curiosity, resilience, and the ability to adapt to new challenges quickly.

• There is a strong emphasis on maintaining high standards for data integrity and ethical data handling practices.

• The internship environment encourages experimentation, learning from failures, and continuous improvement.

• The opportunity may serve as a stepping stone to future full-time employment in data science or analytics within the company.

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Key Responsibilities:

Data Analysis and Exploration:

• Collaborate with teams to analyze extensive datasets and derive actionable business insights.

• Conduct exploratory data analysis to identify patterns, anomalies, and key variables of interest.

• Support storytelling through data by helping convert raw information into meaningful narratives.

• Assist in translating analytical findings into practical business suggestions and recommendations.

Machine Learning and Predictive Modeling:

• Partner with team members to build and test machine learning models suitable for business use cases.

• Experiment with various algorithms and measure their effectiveness based on established metrics.

• Help fine-tune models to improve their prediction accuracy and generalizability.

• Support the development of end-to-end pipelines for deploying machine learning models into production environments.

Data Cleaning and Preprocessing:

• Engage in preprocessing of data, addressing inconsistencies, null values, and format issues.

• Prepare structured and unstructured data for use in analytics and modeling tasks.

• Assist in ensuring datasets are accurate, comprehensive, and suitable for modeling purposes.

• Learn to apply best practices in data wrangling, using tools and libraries appropriate for large-scale datasets.

Statistical Analysis and Experimentation:

• Perform basic statistical analysis to verify assumptions and validate experimental results.

• Help design small-scale experiments to test hypotheses that influence business decisions.

• Learn to interpret and present statistical outputs in an understandable format.

• Contribute to the measurement and evaluation of business KPIs using statistical methods.

Feature Engineering and Data Transformation:

• Work on transforming raw data into usable formats for modeling by creating relevant features.

• Learn techniques like normalization, one-hot encoding, binning, and dimensionality reduction.

• Evaluate the importance of various features using selection methods such as correlation analysis and recursive feature elimination.

• Support the development of high-performance models by enhancing data representation.

Data Visualization and Communication:

• Create meaningful and aesthetically pleasing visualizations using PowerBI, matplotlib, seaborn, or other tools.

• Assist in building dashboards that provide insights to various stakeholders across the organization.

• Summarize key findings in presentation slides and documentation for both technical and non-technical audiences.

• Learn to use data storytelling to explain complex analyses in a compelling and understandable manner.

Collaboration and Learning:

• Participate in daily team activities and meetings to stay updated with ongoing tasks.

• Collaborate with cross-functional departments to gather requirements and understand data sources.

• Seek feedback from peers and mentors to refine analysis and improve output quality.

• Stay up to date with current advancements in data science, AI, and machine learning technologies.

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