Senior Scientist in Cell Culture Development

Sanofi•Framingham, MA

7d•Hybrid

About The Position

Sanofi is looking for a Cell Culture Modelling & AI Development Senior Scientist to join the Commercial Cell Culture Development Department located at Framingham, MA. The Global Cell Culture Development group within Sanofi operates with the mission to develop robust, scalable, and innovative cell culture processes for Sanofi's diverse early and late-stage biologics pipeline. We are looking for a candidate with excellent scientific and computational skills to join a team of upstream development scientists, engineers, and modellers. The ideal candidate will serve as a modelling and AI leader within the group, owning the end-to-end development and deployment of predictive and mechanistic models while maintaining hands-on CHO cell culture expertise. They will bring a strong biotechnology or bioprocess engineering background alongside deep proficiency in computational modelling, machine learning, and AI implementation — with a passion for driving data-driven decision-making and accelerating cell culture development through advanced modelling solutions. 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

Master's degree with 2+ years of experience OR PhD with 0+ years of experience in Biotechnology, Biology, Biochemistry, Chemical Engineering, Computational Biology, or a related discipline (academic experience will be considered)
Demonstrated modelling focus in one or more of mechanistic cell models, machine learning, AI, or multivariate analysis, with tangible project outputs and deployed solutions.
Demonstrated expertise in computational modelling applied to bioprocess or biological systems, with a track record of implementing models that directly influenced process decisions at program or platform level.
Strong background in AI tool development and implementation, including model packaging, deployment, and integration into scientific workflows.
Deep experience with mechanistic modelling (e.g., kinetic models, Monod-type growth models, metabolic flux analysis) and/or machine learning methods (regression, classification, neural networks, Gaussian process regression).
Strong command of statistical methods and experimental design: DoE, PCA/PLS, multivariate data analysis.
Advanced to expert level experience with mechanistic (kinetic, metabolic), ML (random forest, neural networks, GPR), and hybrid modelling approaches.
Proficiency in AI/ML frameworks and familiarity with generative AI tools and LLM applications in scientific workflows.
Cloud Computing: Experience with cloud platforms and MLOps tooling for model deployment, versioning, and management
Track record of implementing models that influenced process decisions at program or platform level.
Statistical Methods: DoE, PCA, PLS, regression, multivariate analysis.
Bioprocess Tools: MVDA software (SIMCA, JMP), LIMS, bioreactor data systems.
Cell Culture: CHO bioreactor operation (HTP, benchtop, pilot scale), standard analytical methods.
Modelling thought leadership: ability to define and advocate for a modelling vision across scientific and business stakeholders.
Strategic thinking and ability to influence modelling adoption across functions and organizational levels.
Stakeholder management and presentation skills, including experience communicating to senior leadership.
Project management capabilities with demonstrated ability to balance and prioritize multiple concurrent modelling and experimental projects.
Collaborative mindset with experience working at the interface of experimental and computational teams.
Strong scientific curiosity and drive to push the boundaries of AI and modelling in bioprocess science.
Ability to work with aggressive timelines and adapt to rapid changes in project priorities.

Nice To Haves

Hands-on experience in CHO or mammalian cell culture process development, with the ability to execute experiments and interpret results independently.
Experience with transfer learning and Bayesian optimization approaches across datasets.

Responsibilities

Drive the modelling strategy across multiple concurrent projects and programs, setting direction for mechanistic, statistical, hybrid, and machine learning approaches in cell culture development.
Lead AI solution development and implementation for cell culture processes, identifying high-impact opportunities and translating them into deployed, production-ready tools.
Mentor junior scientists and engineers in modelling methodologies, AI tools, and data science workflows, fostering a strong modelling culture within the team.
Own the end-to-end model lifecycle — from problem framing, data curation, and model design through validation, deployment, monitoring, and continuous improvement.
Present technical findings, modelling results, and strategic recommendations to senior leadership and cross-functional stakeholders.
Contribute to regulatory filings, technical documentation, and CMC packages, ensuring modelling approaches meet scientific rigor and regulatory expectations.
Establish and champion modelling standards, best practices, and governance frameworks to ensure reproducibility, traceability, and scientific rigor across all modelling activities.
Influence departmental strategy for digitalization and modelling integration, serving as a key voice in shaping the future of upstream process development capabilities.
Lead and design complex CHO upstream process development studies at lab scale, defining experimental strategy and aligning approaches to broader program goals.
Represent Sanofi externally at industry conferences, in publications, and through collaborative partnerships, building Sanofi's scientific reputation in bioprocess modelling and AI.
Manage timelines and deliverables across multiple concurrent projects, ensuring alignment with program milestones and organizational priorities.
Prepare experimental protocols, perform troubleshooting, and author high-quality technical reports and scientific documentation.