Data Science Intern

## Role Summary URBN is seeking a Data Science Intern who will support the Data Science team. You will design, build, evaluate, and support deployment of machine learning models and systems. This includes working closely with other data scientists, ML engineers, and data engineers to perform different tasks. About the Summer Internship Program: Each summer, URBN Inc. welcomes candidates from across the country to immerse themselves within various teams across our portfolio of brands and gain hands-on learning experience in the area of business they choose to pursue. Our paid internship program also offers a summer full of internfocused events, including exposure to senior leadership through small-group settings, insight into company initiatives, off-site field trips, and, for those who qualify, the opportunity to interview for a full-time position. The URBN 2027 Summer Internship Program will run from 6/7 through 8/13 at our Home Office in Philadelphia, PA. ## Role Responsibilities - ML Modeling: Evaluate and build statistical and machine learning models with an emphasis on business impact and time-to-production. Approaches span from basic regressions to advanced neural networks. - Analysis: Support opportunities to add value to the business through application of statistical and ML techniques. Quantify the impact of those opportunities and communicate the outcome to peers and leaders. - ML Deployment: Support ML engineers in deploying data science algorithms in production. This includes containerizing algorithm training and inference workflows, building data pipelines, and monitoring already-deployed ML systems. ## Role Qualifications Machine Learning Techniques: - Understanding of machine learning algorithms, statistical analysis, and predictive modeling applied to real-world business use cases - Proficiency in one or more analytics methodologies such as time series analysis, clustering, and classification.Machine Learning Tools: - Proficiency in SQL. - Familiarity with relevant data science libraries and tools (e.g., Pandas, NumPy, scikit-learn, ggplot2). - Hands-on experience with machine learning frameworks like TensorFlow, PyTorch, Keras, etc.