Analytics Intern at Novartis International AG

Requisition ID.: 336102BR
Employment Type: Internship
Division: Pharma
Business Unit: Global Health PH
Shift Work: No
Functional Area: Interns / Students on Novartis Payroll

Job Description

• 769 million. That’s how many lives our products touched in 2020. And while we’re proud of that fact, in this world of digital and technological transformation, we must also ask ourselves this: how can we continue to improve and extend even more people’s lives?
• We believe the answers are found when curious, courageous and collaborative people like you are brought together in an inspiring environment. Where you’re given opportunities to explore the power of digital and data. Where you’re empowered to risk failure by taking smart risks, and where you’re surrounded by people who share your determination to tackle the world’s toughest medical challenges.

Duties
Imagine what you could do at Novartis!

• 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 a 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.

Minimum Requirements

• 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, SAS or strong background in NONMEM and/or other similar software/languages are preferred.