Translational Medicine AI/ML Summer Intern

Sanofi Group; Morristown (NJ); On-site; USD 68,000 - 97,000; Full time

Job summary

Sanofi Morristown, NJ invites a Summer Intern to join the EGDS (Evidence Generation and Decision Science) group. You will contribute to design, conduct, and analysis of early- and late-phase trials under mentorship from experienced biostatisticians.

This role emphasizes methodological research, AI/ML modeling, and real-world data analytics to generate insights and improve trial efficiency, with opportunities to learn across the drug development lifecycle.

Qualifications

• Two years of graduate coursework and working on a dissertation toward a PhD in statistics or biostatistics.
• Must be permanently authorized to work in the U.S. and not require sponsorship of an employment visa at the time of application or in the future.
• 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.

Responsibilities

• The intern will contribute to the design, conduct, and analysis of early- and late-phase clinical trials while engaging in methodological research under the mentorship of experienced biostatisticians.
• The candidate will be involved in a range of activities, including literature review, development of computational tools, and implementation of state-of-the-art AI/ML modeling and simulation approaches in clinical trials.

Skills

Statistical analysis

Data visualization

Machine learning

AI tools

Education

PhD in statistics or biostatistics

Job description

Sanofi Morristown, NJ invites a Summer Intern to join the EGDS (Evidence Generation and Decision Science) group. You will contribute to design, conduct, and analysis of early- and late-phase trials under mentorship from experienced biostatisticians.

This role emphasizes methodological research, AI/ML modeling, and real-world data analytics to generate insights and improve trial efficiency, with opportunities to learn across the drug development lifecycle.