Data Science Intern — Summer 2027
GallupAbout the role
Apply early for priority consideration. Gallup has limited openings for this internship, and the position will be filled quickly. As a data science intern at Gallup, you’ll turn data into insights that help product leaders make smarter decisions. Working alongside Gallup data scientists and product managers, you’ll analyze data to help answer real product challenges, uncover patterns in how people engage with our products, and translate your findings into recommendations that influence what we build and how we grow. You’ll also have opportunities to collaborate with marketing partners, using data to better understand customers, sharpen targeting and identify new opportunities for growth. What You’ll Do
During your internship, you will
Analyze product and customer data to uncover patterns, answer business questions and inform product decisions Use Python, SQL and statistical methods to explore data, build analyses and translate findings into actionable insights Support product analytics for CliftonStrengths®, contributing to projects across its growing portfolio of products and touchpoints Partner with data scientists, product managers and other stakeholders to conduct analyses and communicate findings Explore opportunities to use data to support marketing efforts, including understanding customers better and identifying opportunities to improve targeting and sales Apply modern data science and AI tools to work efficiently, experiment with new approaches and strengthen the quality of your analysis What You Need Enrollment in a bachelor’s or graduate degree program in computer science, statistics, data science, engineering, math or a related field required Classroom or work experience coding in a data science environment using Python and its data science libraries (e.g., pandas, NumPy, scikit-learn, PyTorch, Matplotlib) required Aptitude in programmatic statistical analysis and modeling (univariate analysis or multivariate analysis) required Experience with nonpara
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