2027 Spring Co-op Process and CMC Data Science, Waltham, MA

Sanofi•Waltham, MA

1d•$27 - $60•Onsite

About The Position

Sanofi is seeking a highly motivated Spring Co-op to join the Process and CMC Data Science team in the Data and Computational Science department within its mRNA Center of Excellence in Waltham, Massachusetts. The six-month co-op focuses on developing first-principle, machine-learning, and hybrid models—including Computational Fluid Dynamics, Molecular Dynamics, and Quantum Chemistry simulations—for drug-product unit operations such as T-mixing and Tangential Flow Filtration. The work is intended to accelerate mRNA-based process development. The co-op offers hands-on experience with mRNA technology, drug-product formulation, and fill-finish operations, applying computational science and process-engineering methods in biopharmaceutical research. The student will work with a supervisor and cross-functional team members and contribute modeling insights that inform process-development decisions. The role requires relocation to the office and a 40-hour workweek, Monday through Friday, for the full co-op duration.

Requirements

Pursuing a Master's or PhD in Chemical Engineering, Biochemical Engineering, Mechanical Engineering, Computational Science, Biomedical Engineering, Applied Mathematics, or a related quantitative field at an accredited college or university.
Must be permanently authorized to work in the U.S. and not require employment-visa sponsorship at application or in the future. Students on CPT, OPT, or STEM OPT generally do not meet this requirement unless eligible for an alternative long-term status that does not require company sponsorship.
Must remain enrolled in an accredited college or university throughout the co-op/internship.
Must be able to relocate to the office location and work 40 hours per week, Monday through Friday, for the full duration of the co-op/internship.

Nice To Haves

Foundational knowledge of mRNA technology, lipid nanoparticle formulation, or biopharmaceutical manufacturing processes.
Experience developing physics-informed or physics-constrained machine learning, first-principle models including CFD (Ansys Fluent), Molecular Dynamics (GROMACS, LAMMPS, VMD), or Quantum Chemistry (Gaussian, ORCA) simulations, or hybrid dynamic models for physical or biological systems.
Proficiency in one or more modeling and programming tools: Ansys Fluent, Python (required), BOTorch, PYOMO, Julia, MATLAB, or gPROMS.
Familiarity with Design of Experiments, uncertainty quantification, or Bayesian optimization.
Strong written and verbal communication skills, including the ability to present technical results to diverse audiences.
Minimum cumulative GPA of 3.0 on a 4.0 scale.

Responsibilities

Develop and validate first-principle, machine-learning, and hybrid models for drug-product unit operations such as T-mixing and Tangential Flow Filtration, in collaboration with a supervisor and cross-functional team members.
Apply CFD, Molecular Dynamics, and/or Quantum Chemistry simulations to characterize mixing behavior, nanoparticle formation, or molecular-level interactions relevant to lipid nanoparticle formulation.
Investigate the impact of process parameters on critical quality attributes and quantify associated uncertainties using statistical and computational methods.
Design and execute Design of Experiments for model structure selection, parameter estimation, and process optimization.
Analyze experimental and simulation data to generate actionable insights that inform process-development decisions.
Document modeling workflows, results, and recommendations in technical reports and presentations.