Interns will utilize statistics and computer science to solve complex problems in healthcare. Problem areas include: diagnostic analytics such as image analysis, signal processing and pattern recognition, NLP, scheduling, risk analysis and prevention, outcome analysis, targeting and socio-economic behaviors, personalization – including sentiment, interests and affinities modeling, and process improvement (e.g. making sure patients are referred to the care teams they need). The Intern will interact with vast amounts of clinical and non-clinical data (external data sources, digital, etc.). Interns will learn the data science lifecycle including data acquisition, transformation, data engineering, exploratory data analysis, feature engineering, algorithm development, and algorithm deployment into a relevant clinical and/or business context. Interns are expected to help formulate analytical questions, perform hypothesis testing, algorithm optimization, algorithm deployment, and integrate feedback rapidly from stakeholders. Candidate needs to have an open mind and ability to take concepts developed by other industries and apply them to healthcare. Candidates should be effective communicators and good team players. Candidates who have coursework, projects, and evidence of interest in data science (e.g. participating in Kaggle competitions) but lack industry experience are ideal for this position.