We're Hiring!

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AI Fairness in Medicine Internship

Position Description:

Recent success stories of using artificial intelligence for diagnosing skin cancer, diabetic retinopathy and pneumonia have given the impression that such methods are on the cusp of revolutionizing medicine. However, many of the most important problems, such as predicting disease progression, personalized medicine and drug discovery, all require a fundamentally different way of thinking. Specifically, these problems require a focus on fairness, bias, transparency, and robustness. As we observe an increasing interest in the development of automated risk stratification and disease classification models in the clinical practice, it is crucial to address such aspects. 

We are seeking candidates for a 6-12 month internship in Trustworthy/Fair AI.

AI Fairness interns  will work with clinicians, AI architects , software engineers, and our partner companies to develop and deploy  an end-to-end platform in which researchers can measure latent inequities and biases in both their datasets and clinical models in an explicit and intuitive manner such that they can be addressed prior to real-world deployment. In essence, provide tools by which clinicians and scientists can construct fair (non-discriminatory) and robust AI model that are ridden of spurious correlations and that are generalizable to new subpopulations and underrepresented demographics.

Are you passionate about working on compelling, real-world problems? At BAGIL, our vision is to use data science and artificial intelligence to transform medicine and healthcare delivery into a data-driven and evidence- based discipline that is personalized for each individual patient. 

Candidates must have expertise in AI fairness, AI ethics, Trustworthy AI, machine learning, deep learning, statistical data processing, regression techniques, neural networks, decision trees, clustering, pattern recognition, probability theory and data science methods used to analyze data. 

Role & Responsibilities:

Interns will utilize statistics and computer science to solve complex problems. The AI Fairness intern will interact with vast amounts of clinical and non-clinical data (external data sources, digital, etc.). Interns will learn the AI Engineering 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 clinical 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.  

Qualifications:

• Current enrollment or recent graduate of undergrad or grad program in: Computer Science, Information Technology, Engineering, 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.

If you are interested in transforming medicine and joining BAGIL please submit your resume/CV with cover letter.