2018 Experiential Intern (Graduate Level) - Health Economics & Outcomes Research (HEOR) Analytics

Lake County, Illinois, USA
Apr 03, 2018
Required Education
Bachelors Degree
Position Type
Full time
The AbbVie Experiential Internship Program As an AbbVie Experiential Intern, you'll participate in a paid, ten to twelve-week summer program that is focused on providing students with robust projects throughout the summer. As an intern, you will be located at our corporate headquarters in north suburban Chicago, with housing and shuttle services for eligible students. Department Overview - Health Economics and Outcomes Research (HEOR) Analytics The HEOR department is responsible for evaluating the economic, clinical and humanistic value of medical technologies. HEOR strives to:
  • Provide evidence of value to other stakeholders to ensure patients have access to innovative therapies
  • Develop and guide pricing & reimbursement strategies based on evaluation of public health policy & reimbursement environment and scientific collection & analysis of clinical, economic and patient-reported outcomes data
Assignment Details This summer intern position is to support HEOR analytics team. Develop an algorithm using machine learning method for Health Economics and Outcomes Research (HEOR) related projects. Develop analysis plan and analyze using large medical claims databases. Review literature for innovative advanced analytics methods Qualifications Basic:
  • Completed at least one year of college education before beginning internship
  • Must be enrolled in school the semester following your internship
  • Graduate Level Student - seeking a Ph.D in statistics, applied mathematics, Data science or a related field in a US based school
  • Minimum Cumulative GPA: 3.0/4.0
  • Must be authorized to work in the U.S. on a permanent basis without requiring sponsorship (students on an F1 visa with CPT can be accommodated).
  • Coursework in machine learning, statistics, and/or predictive modeling
  • Experience in SAS, R and Python
  • Medical or pharmaceuticals knowledge
  • Research experience in explaining behavior of complex machine learning models
  • Experience with data transformations for feature engineering including automatic feature extraction and manual feature construction

Equal Opportunity Employer Minorities/Women/Veterans/Disabled