Senior Scientist Automation and Advanced Modeling

Sanofi•Cambridge, MA

2d•$100,500 - $145,167•Hybrid

About The Position

We are seeking a highly motivated individual to join our drug substance process engineering and modeling team. As a technical expert in process engineering and modeling, you will play a key role in advancing our global engineering department within Sanofi’s R&D CMC Synthetic organization. This position is located in Cambridge MA and will collaborate closely with colleagues in France to support digital development and implement new ways of working. Your expertise in chemical engineering, process system modeling, and lab automation will be crucial in creating a self-driving lab for small molecule drug substance process development and optimization. You will help developing automated experimental workflows to generate data for model building, advancing the creation of digital twins and driving innovation in pharmaceutical process development. We believe that lab automation and digital development are crucial for the future of process development in Pharma. By joining our team, you will be at the forefront of this transformation, using your expertise to push the boundaries of process engineering. This is an exciting opportunity to demonstrate the benefits of process modelling and data science in a cutting-edge scientific department developing the next generation of Sanofi medicines that will improve the quality of life for patients across the world. Join the engine of Sanofi’s mission — where deep immunoscience meets bold, AI-powered research. In R&D, you’ll drive breakthroughs that could turn the impossible into possible for millions.

Requirements

PhD or PhD candidate in chemical engineering, mathematics, physics or related with expected graduation date by first semester of 2027 with 0+ years of experience (academic experience will be considered) OR a master's degree in chemical engineering, mathematics, or physics or related with a minimum of 4 years of experience (academic experience will be considered) OR a bachelor's degree in chemical engineering, mathematics, or physics with a minimum of 8 years of experience.
Strong skills in writing and optimizing scripts in programming languages such as Python/Matlab/C#/Visual Basic.
Experience with crystallization modeling and/or simulation tools (e.g., gPROMS).
Experience in designing and implementing automated laboratory processes from the initial concept through to full deployment.
Demonstrated ability to translate mathematical models into computer programs.
An ability to work as part of a team, engaging other scientists with complementary skill sets in the field of pharmaceutical development.
A change agent mentality, proposing novel approach to challenging scientific questions and exploring new modeling approaches.

Nice To Haves

Familiarity with laboratory analytical measurement instruments or relevant work experience in a pharmaceutical sciences laboratory setting.
Domain knowledge in small molecule process development with demonstrated industrial applications or academic publications in this field.
Some knowledge in Multivariate Analysis, chemometrics and statistics.
Experience in the development and scale-up of continuous processes.

Responsibilities

Design, develop, and deploy cutting-edge automated systems, robotics, instrumentation, and digital workflows that significantly enhance the productivity and efficiency of our labs.
Programming and developing automated laboratory systems.
Supporting software development and deployment to automate workflows.
Integrating analytical instruments with laboratory software.
Ensuring seamless communication between instruments and Sanofi's internal data management services.
Build mechanistic, hybrid, and data-driven modeling solutions to support small molecule process development.
Collaborate with process development teams to integrate crystallization modeling into the overall process design and optimization workflow.
Assist in the development and deployment of digital twins for both batch and continuous processes, ensuring alignment with Quality by Design (QbD) principles.
Implement and scale Model Predictive Control (MPC) systems, leveraging advanced process models to optimize performance in real-time, maintain product quality, and enhance operational efficiency across large-scale manufacturing environments.
Provide concise technical presentations to communicate work to project teams, cross functional teams and management.
Document all work on projects and continuous improvements in eLNB experiments and summarize in Technical Reports.
Establish/extend external network by providing leadership on academic, industrial, or government sponsored collaborations.
Steer collaboration projects to address gaps in scientific knowledge.