Stats Perform has an exciting opportunity for an AI Intern. Our summer internship program is a unpaid 3 months part-time internship. Interns work together and individually within the organization to gain valuable experience and make an impact in sports technology. Not only will Stats Perform Interns have hands on experience in their field of study but there are additional intern activities that include learning sessions, skills building workshops, social events and speakers. At Stats Perform, we have a world-class artificial intelligence team which aims to maximize the value of the enormous amount of sports data that we have at our disposal. As an Artificial Intelligence Intern, you will work with our AI researchers on any one of a wide range of interesting projects at Stats Perform, which span the following fields:Computer Vision/Machine Learning Predictive Analytics/Multi-scale Simulation & Optimization Natural Language Processing/UnderstandingResponsibilities:Create and develop new ideas in a cross-functional role of research and development. Engage with domain experts as well as senior leaders and engineers to effectively deliver interpretable results. Deal with enormous amounts of unstructured and/or structure data and developing solutions in a distributed setting. Do analysis on sports related problems and create solutions to address these interesting problems.Required Qualifications:To be currently working towards a degree in Machine Learning, Computer Vision or related (e.g., Statistics, Mathematics, Physics, Operations Research, Computer Science) – outstanding undergraduate candidates will be considered as well with a shown track record in the above fields. Substantial real-world experience with unstructured data sources, especially in areas of spatiotemporal data and/or vision. Expertise in predictive analytics/statistical modeling/data mining algorithms Knowledge/experience in some/all of the following: Multivariate Regression, Logistic Regression, Support Vector Machines, Bagging, Boosting, Decision Trees, common clustering algorithms, Optimization, Stochastic Processes, Recommendation Systems, Metric Learning, Multi-Task/Transfer Learning, Active Learning, Hierarchical/Deep Learning.