Summer Intern, Clinical Pharmacology & Pharmacometrics (focus on Statistics)
The students will work under the guidance of experienced drug development scientists on the application of state-of-the-art quantitative tools within the modeling and simulation framework, including Bayesian application/methodologies to support pediatric drug development for cardiovascular disease or application of machine learning for logistic regression analysis to support oncology drug development. As part of the Bristol Myers Squibb Virtual Intern Program, the successful candidates will have cross-functional networking opportunities for personal and professional development.
Key Responsibilities
- Collaborate with the CPP and project teams in designing quantitative analysis plans to address clinical pharmacology related questions
- Coordinate with data scientists and biostatisticians in preparing for in-house clinical analysis datasets
- Develop state-of-the-art quantitative tools within the modeling and simulation framework, including Bayesian extrapolation to support pediatric drug development for cardiovascular disease or application of machine learning for logistic regression analysis to support oncology drug development
- Interact and present findings to CPP and project teams
Qualifications & Experience
- Candidate should be enrolled in a graduate program (Ph.D., M.S., PharmD) in Biostatistics, Pharmaceutical Sciences/Pharmacy, Bioinformatics, Biomedical Sciences, Engineering or equivalent with an interest in quantitative applications in drug development
- Ideal candidate should have a good familiarity with quantitative methods and/or modeling in one-two domains among the following: statistical, mathematical, biological, pharmacological
- Intermediate R (and/or SAS) programming skills are required
- Competitive candidates should have excellent oral and written communications skills and strong problem-solving skills
- Candidates must be available for 10-12 weeks from June - August and must have the ability to work full-time during that time.