AI/ML Intern - Radiation Oncology

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. Current enrollment or recent graduate of undergrad or grad program in: Computer Science, Information Technology, Data Science, Physics, Statistics/Biostatistics, or other related field with strong quantitative analytical skills. School or project experience in applying data science approaches/tools. Proven written and oral communication skills are essential.

AI experience/exposure or familiarity with AI principles is required. Courses and comfort level with at least one of the following: computer programming, machine learning, graph theory, algorithms, statistics, SQL and linear algebra are essential for success. Experience with major programming languages is beneficial. Demonstrated initiative/innovation in coursework and projects. Experience with data science development tools building algorithms on structured data is preferred. Experience working with unstructured data such as natural language, images, genetics, or signal processing (e.g. EKG) is preferred. Experience programming in Python is essential. Experience with C/C++/Java or other systems programming languages and software engineering methodology is not required but beneficial. Candidates with exceptional web-stack data visualization (Angular/D3) experience will also be considered.