Interns will work on the application or development of statistical, machine learning or pharmacokinetic / pharmacodynamics modeling methods in drug development, under the supervision of a senior-level quantitative scientist.
Depending on nature of specific drug development questions, interns may employ variety of quantitative methods, including machine learning (e.g., deep learning), survival analyses, causal inference, Bayesian statistics, disease progression modeling and pharmacokinetic/pharmacodynamics modeling, among other quantitative methods.
Interns will also attend seminars and other activities to enhance their understanding of the drug development process. There will be opportunities for interns to present their project results to a team of quantitative scientists and other key stakeholders.
Candidates should be graduate-level students in biostatistics, statistics, pharmacometrics, engineering or in a related discipline
Competitive candidates must have excellent oral and written communication skills and strong problem-solving skills.
Preferred Competencies / Experiences:
Candidates with working knowledge of R are preferred.