Post Doctoral Fellow - Upstream Development

Post Doctoral Fellow - Upstream Development

We deliver 4.3 billion healthcare solutions to people every year, thanks to the flawless planning and meticulous eye for detail of our Manufacturing & Supply teams. With your talent and ambition, we can do even more to protect people from infectious diseases and bring hope to patients and their families.

Sanofi's Global MSAT (Manufacturing Sciences, Analytics, and Technology) acts as a crucial link between our R&D and Manufacturing entities, playing an instrumental role in enhancing our current portfolio and delivering future launches of high-quality and innovative drugs and vaccines. Global MSAT is a beacon of innovation and process excellence in Manufacturing, this includes guiding the journey of new products from their launch to commercial success, being at the forefront of Data Sciences and Digitalization. Join a team of talented and passionate international experts, scientists, and project leaders dedicated to inventing, designing, industrializing, and implementing cutting-edge industrial chemical and biological processes, analytical methods, and process modeling to successfully launch 3 to 5 new products by year.

The Post-doctoral / Scientist I position is part of the Upstream team in the Recombinant Drug Substance (DS) function within global MSAT and is based at the Mammalian Platform hub in Framingham, MA. The Recombinant DS function within global MSAT is the owner of Life Cycle Management (LCM) of DS processes. Implementation of second-generation processes is one of the top performance enablers for Sanofi's biologics manufacturing network and integral to LCM. We deliver innovative, robust, and cost-effective next generation processes. In recent years we have successfully established second-generation manufacturing mammalian processes with a continuous process platform. The function is responsible for tech transfer, process validation, dossier preparation, & PAI support to enable launch of new and LCM products. We are pursuing future innovations such as digital labs, factory of the future, and advanced-analytics-based process understanding and control.

The Scientist in this role will provide expert development of hybrid models and technical support to: (1) predict and optimize mammalian cell culture process parameters for enhanced cell growth and metabolism control, and (2) improve recombinant protein expression while maintaining desired product quality attributes (e.g., glycosylation patterns) in perfusion or fed-batch cultures. The candidate will conduct internal studies and collaborate with academic institutions and internal teams on developing hybrid models composed of first principle-based models such as metabolic flux analysis or genome-scale models and data-driven models such as neural networks. Additionally, the candidate will integrate Raman spectroscopy technology with the Raman team into the hybrid model. This individual will work in a team environment, collaborating with various colleagues within and outside the organization to develop upstream cell culture processes using state-of-the-art upstream lab systems.

We are an innovative global healthcare company with one purpose: to chase the miracles of science to improve people's lives. We're also a company where you can flourish and grow your career, with countless opportunities to explore, make connections with people, and stretch the limits of what you thought was possible. Ready to get started?

Main Responsibilities

• Develop hybrid models integrating first principle approaches (metabolic flux analysis, genome-scale models) and machine learning algorithms (neural network model), with primary focus on predicting cell culture performance and protein product quality attributes.
• Apply and refine these predictive models to optimize mammalian cell culture performance parameters.
• Implement real-time control strategies using hybrid models to optimize protein quality and yield in perfusion or fed-batch cultures.
• Work with team to integrate multimodal PAT (process analytical technology) data streams, including Raman spectroscopy, into process monitoring and control systems.
• Collaborate with academic partners to advance mechanistic modeling of glycosylation and dynamic metabolic flux analysis.

About You

Experience:

• PhD candidate expected to complete degree prior to starting, or PhD graduate in chemical engineering, computational science, biology, biochemistry, bioinformatics, or closely related field
• Hands-on experience at laboratory in biological experiments
• Background in computational simulation and programming

Soft and Technical Skills:

• Scientific writing and presentations skills
• Highly motivated individual with attention to detail, able to work both independently and in cross-functional teams
• Flexible with ability to adjust to a fast-paced, goal-oriented environment and willingness to learn
• Knowledge in one or more areas of metabolic flux analysis, genome scale modeling, or machine learning applications as applied to cell culture systems
• Experience with Python programming and statistical analysis tools.
• Understanding of mammalian cell culture processes and protein production (preferred)

Languages:

• English

Why Choose Us?

• Bring the miracles of science to life alongside a supportive, future-focused team.
• Discover endless opportunities to grow your talent and drive your career, whether it's through a promotion or lateral move, at home or internationally.
• Enjoy a thoughtful, well-crafted rewards package that recognizes your contribution and amplifies your impact.
• Take good care of yourself and your family, with a wide range of health and wellbeing benefits including high-quality healthcare, prevention and wellness programs and at least 14 weeks' gender-neutral parental leave.

The salary for this position is $95,000 annually. All compensation will be determined commensurate with demonstrated experience. Employees may be eligible to participate in Company employee benefit programs. Additional benefits information can be found through LINK.