Spring Co-op: Quantitative Pharmacology & AI in Pharma

Sanofi; Morristown (NJ); On-site; USD 79,000 - 83,000; Full time

Job summary

Sanofi in Morristown, NJ invites a Spring Co-op to join the Quantitative Pharmacology team. You will work on building and applying quantitative models, simulating scenarios, and advancing AI/ML–augmented pharmacometrics workflows under senior mentorship.

The role emphasizes real-world modeling within Translational Medicine, literature review, and development of computational tools. Ideal candidates are PhD students in quantitative fields, eager to learn and contribute across a multidisciplinary

Qualifications

• PhD candidate in statistics, biostatistics, biomedical engineering, applied mathematics, or related quantitative discipline.
• Must be permanently authorized to work in the U.S. without visa sponsorship at application time or in the future.
• Must be enrolled in an accredited college or university for the duration of the co-op/internship.
• Must relocate to Morristown, NJ and work 40hrs/week for the full duration.

Responsibilities

• Develop and apply quantitative, mathematical, and statistical models and simulations with experienced scientists.
• Explore AI/ML opportunities to augment pharmacometrics workflows within a quantitative framework.
• Conduct literature reviews and build computational tools for model-informed decision support.

Skills

PK/PD

Pharmacometry

Statistics

Machine learning

Data modeling

Data visualization

Python

R

AI/ML tools

Education

PhD candidate in statistics/biostatistics/biomedical engineering/applied mathematics

Tools

Python

R

Matlab

Job description

Sanofi in Morristown, NJ invites a Spring Co-op to join the Quantitative Pharmacology team. You will work on building and applying quantitative models, simulating scenarios, and advancing AI/ML–augmented pharmacometrics workflows under senior mentorship.

The role emphasizes real-world modeling within Translational Medicine, literature review, and development of computational tools. Ideal candidates are PhD students in quantitative fields, eager to learn and contribute across a multidisciplinary