Development Graduate Intern

Interns will utilize statistics and machine learning concepts to solve complex problems in philanthropy. Problem areas include: predictive and prescriptive analytics, pattern recognition, NLP, risk analysis and prevention, outcome analysis, targeting and socio-economic behaviors, personalization – including sentiment, interests and affinities modeling, and process improvement. 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, visualization, feature engineering, algorithm development, and algorithm deployment into a 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 philanthropy. 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. Candidate needs to have a schedule that accommodates an approximately six-month, full-time internship. Candidate should stay abreast of emerging trends and technologies in data science and contribute innovative ideas to the team.

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.

The ideal candidate will possess: 

• Strong foundation in statistical analysis, machine learning algorithms, and data modeling 
• Experience with data visualization tools 
• Familiarity with SQL and experience working with databases 
• Excellent problem-solving abilities and analytical skills 
• Ability to work independently as well as part of a team in a fast-paced environment 
• Strong communication skills, with the ability to present complex information to non-technical audiences 

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