2027 Spring Co op Process and CMC Data Science Waltham MA

About the Role

• A 6-month co-op position in the Process and CMC Data Science team.
• Located in the Data and Computational Science (DCS) department within Sanofi's mRNA Center of Excellence (mRNA CoE).
• Focuses on developing models for critical drug-substance unit operations.

Key Responsibilities

• Develop and validate first-principle, machine-learning (ML), and hybrid models for critical drug-substance unit operations including In-Vitro Transcription (IVT), Tangential Flow Filtration (TFF), and Chromatography.
• Investigate the impact of process parameters on critical quality attributes (CQAs) and quantify associated uncertainties using statistical and computational methods.
• Design and execute Design of Experiments (DoE) 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 clear technical reports and presentations.

Requirements & Tech Stack

• Python (must-have)
• BOTorch
• PYOMO
• MATLAB (nice to have)
• Julia (nice to have)
• gPROMS (nice to have)

Qualifications

• Pursuing a Master's or PhD in Chemical Engineering, Biochemical Engineering, Biochemistry, Data Science, Computer Science, Biomedical Engineering, Applied Mathematics, Analytical Chemistry, Physics, Software Engineering or a related quantitative field at an accredited college or university.
• Must be permanently authorized to work in the U.S. without requiring future sponsorship.
• Must be enrolled in an accredited college or university throughout the duration of the co-op/internship.
• Must be able to relocate to Waltham, MA and work 40 hours/week, Monday to Friday, for the full duration of the co-op/internship.
• Minimum cumulative GPA of 3.0 on a 4.0 scale.

Preferred Qualifications

• Foundational knowledge of mRNA technology or biopharmaceutical manufacturing processes.
• Experience developing physics-informed or physics-constrained ML, first-principle, or hybrid dynamic models for physical or biological systems.
• Familiarity with Design of Experiments (DoE), uncertainty quantification, and Bayesian optimization.
• Strong written and verbal communication skills with the ability to present technical results to diverse audiences.
• (Implied from "Nice to have: First-principle models, machine-learning, chromatography optimization, ML models para IVT, purification, chromatography processes") Experience with first-principle models, machine-learning, chromatography optimization, and ML models for IVT, purification, and chromatography processes.

Compensation

• $35.00 - $60.00 hourly.