Bristol-Myers Squibb Company


Princeton, NJ, United States
Jul 11, 2019
Required Education
Masters Degree/MBA
Position Type
Full time
  • Conduct non-interventional research projects using retrospective data resources in support of CORDS customer needs
  • Communicate project status and results effectively to all stakeholders
  • Engage with customer teams on protocol development, statistical analysis, interpretation and presentation of results and strategic direction of messaging and research needs.
  • Provide scientific leadership and guidance related to health and economic outcomes research using non-interventional methods
  • With guidance from team Lead, assist staff with execution of research projects across customer teams
  • Mentor staff in development of functional and behavioral skills related to job performance.
  • Engage key CORDS customers in research and market access needs assessment in order to plan and execute value-focused research projects
  • Assess information environment and analytical tool set to ensure current needs are met and future needs are communicated for assessment by CORDS Data Development team
  • Complete other duties as assigned by CORDS Lead
  • Masters in biostatistics, epidemiology or related quantitative research field; PhD preferred
  • 5-7 years experience in pharmaceutical outcomes research, pharmaco-epidemiology, health services research or related field
  • 4+ years experience using large retrospective data sets in the conduct of epidemiologic and economic research
  • 4+ years experience with statistical programming using SAS
  • Experience with protocol development and execution for health and economic outcomes research projects
  • Proven strong writing and oral presentation skills
  • 3+ years project leadership experience preferred
  • An ideal candidate will have some experience with the following tools and data analysis methods:
    • Software: SAS, SQL, R, Java, Matlab, C++, Python
    • Data analysis methods: predictive modeling, decision tree analysis, clustering, data mining, data processing, genetic algorithms, machine learning, active machine learning (optimal experimental design), Bayesian optimization