2027 Spring Co-op Machine Learning AI, Cambridge, MA

Sanofi•Cambridge, MA

1d•$47 - $60•Onsite

About The Position

The Quantitative Pharmacology (QP) group in Sanofi is seeking a Machine Learning co-op to implement, develop and validate data-based (AI/ML) models to enhance decision making across drug discovery and development. The focus will be on predicting pharmacology dynamics of large molecules (biologics) by integrating structural properties, preclinical and clinical data. The resulting AI/ML models will aid in early drug development decisions and drive impact across multiple R&D organizations. In support of these activities, the successful incumbent should be able to analyse and interpret preclinical and clinical data, design and train Biology- and Pharmacology-based AI/ML models and support critical decision making as part of the QP team. The QP group supports multiple therapeutic areas and research platforms within the broader R&D organization. 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.

Requirements

Currently enrolled in a Master's or PhD program in a STEM field (e.g. Engineering, Computer Science, Mathematics or related field)
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.
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.
Experience with Python and relevant libraries
Experience with Statistical/Machine Learning

Nice To Haves

Familiarity with basic concepts of drug discovery and development. Focus on biologics preferred.
Good written, presentation and verbal communication skills are essential.
Ability to work in a matrix and in a global environment.
Experience with Large Language models/ deep learning/ time series data modeling preferred

Responsibilities

Implement, develop and validate data-based (AI/ML) models to enhance decision making across drug discovery and development.
Predict pharmacology dynamics of large molecules (biologics) by integrating structural properties, preclinical and clinical data.
Aid in early drug development decisions and drive impact across multiple R&D organizations.
Analyse and interpret preclinical and clinical data.
Design and train Biology- and Pharmacology-based AI/ML models.
Support critical decision making as part of the QP team.