2027 Spring Coop Quantitative Pharmacology Morristown NJ

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

Job Title: 2027 Spring Co-op Quantitative Pharmacology

• Grade: Co-op
• Location: Morristown, NJ
• Duration: 6 months

About the Job

Looking to launch your career at the cutting edge of healthcare? Join Sanofi for a chance to develop with mentoring and guidance from inspirational leaders while helping to make an impact on the lives of countless people worldwide. We are seeking a highly motivated Spring Co-op to join our Quantitative Pharmacology team.

The Quantitative Pharmacology (QP) department is an integral part of the Translational Medicine Unit within Sanofi. We leverage model-informed drug development (MIDD), including population pharmacokinetic (PK) and pharmacodynamic (PD) approaches, to support optimal decision-making and accelerate programs across the full drug development continuum, from early discovery through late-stage clinical development. In addition, we actively explore the integration of artificial intelligence and machine learning (AI/ML) approaches to complement traditional pharmacometrics methods and enhance data-driven decision-making.

Ready to grow your skills while helping shape the future of healthcare? At Sanofi, you'll be empowered to learn, ask questions, and bring your ideas to life - all while supported by inspiring mentors and collaborative teams.

About Sanofi

We're an R&D-driven, AI-powered biopharma company committed to improving people's lives and delivering compelling growth. Our deep understanding of the immune system - and innovative pipeline - enables us to invent medicines and vaccines that treat and protect millions of people around the world. Together, we chase the miracles of science to improve people's lives.

Main Responsibilities:

• The co-op will work closely with experienced scientists to develop and apply quantitative, mathematical, and statistical models and simulations. These efforts will contribute to the advancement of innovative methodologies, including the evaluation and application of AI/ML techniques where appropriate to complement mechanistic and statistical modeling approaches.
• The candidate will be involved in a range of activities, including literature review, development of computational tools, and implementation of state-of-the- art modeling and simulation approaches within a quantitative framework. This may include exploring opportunities where AI/ML methods can augment pharmacometrics workflows, such as in data integration, predictive modeling, and model-informed decision support.

About You

Basic Qualifications:

• We are seeking a PhD candidate in statistics, biostatistics, biomedical engineering, applied mathematics, or a related quantitative discipline, who is advanced in their research program.
• Must be permanently authorized to work in the U.S. and not require sponsorship of an employment visa (e.g., H-1B or green card) at the time of application or in the future. Students currently on CPT, OPT, or STEM OPT usually require future sponsorship for long term employment and do not meet the Internal requirements for this program unless eligible for an alternative long‑term status that does not require company sponsorship.
• Must be enrolled in an accredited college or university throughout the duration of the co‑op/internship.
• Must be able to relocate to the office location and work 40hrs/week, Monday to Friday for the full duration of the co‑op/internship.

Preferred Qualifications:

• Candidates should have a strong academic background in areas such as PK/PD, pharmacometrics, pharmaceutical sciences, applied mathematics, statistics/biostatistics, computational biology, or biomedical engineering.
• Experience in data modeling, data science, data visualization, machine learning, and modern computational tools is highly desirable.
• The ideal candidate will demonstrate the ability to work both independently and collaboratively within a multidisciplinary team environment.

Why Choose Us

• Bring the miracles of science to life alongside a supportive, future focused team.
• Stretch your skills through apprenticeships, internships, graduate programs, and international experiences that grow with you.
• Learn from inspiring mentors and collaborative teams while contributing to real projects that shape the future of healthcare.
• Build your future with access to the latest tools, digital innovation, and continuous learning that keep you ahead of the curve.

Compensation

The salary range for this position is $57 -$60 USD hourly. All compensation will be determined commensurate with demonstrated experience.

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