Senior Scientist - Process Modelling
Sanofi
5 hours ago
Remote friendly (Framingham, MA)
United States
$100,500 - $145,166.66 USD yearly
Operations
Main Responsibilities
- Develop, calibrate, and validate bioreactor dynamic models to simulate, optimize, and control recombinant protein production and product quality attributes.
- Transfer and deploy bioreactor models in a commercial manufacturing environment to support process monitoring and forecasting (digital shadows) and/or real-time bioreactor optimization and control (digital twins).
- Work as part of matrix project teams with cell-culture development, process development, analytical, automation, and data science teams across R&D and manufacturing.
- Communicate model assumptions, uncertainty, limitations, and process interpretation to technical and non-technical stakeholders.
- Maintain reproducible computational workflows using coding, documentation, version control, and data-management practices.
- Act as a change agent to support the implementation of process modeling in established cell culture process development workflows.
- Document progress against deliverables via technical presentations, reports, and—when appropriate—scientific publications.
Qualifications
- Education: PhD in Chemical Engineering, Biochemical Engineering, Process Systems Engineering, Bioengineering, Biomedical Engineering, or related field; OR Master’s in the same fields.
- Experience: PhD—minimum 1 year relevant experience; Master’s—minimum 4 years relevant industry/research experience. Demonstrated experience developing dynamic models of bioreactor or cell culture processes.
- Skills: Proficiency in at least one of Python, MATLAB, Julia, or R.
Preferred Qualifications
- Dynamic mechanistic modeling using ODEs, kinetic rate laws, mass balances, parameter estimation, and model validation.
- Mammalian cell culture development (fed-batch and/or perfusion).
- Bioreactor digital twins/advanced monitoring tools in industrial biopharma.
- Bioreactor engineering principles (oxygen transfer, mixing, mass transfer, CO2 stripping, kLa, shear, scale-up/scale-down).
- Perfusion modeling (cell retention, bleed, media exchange, residence time, productivity, steady-state).
- Multivariate monitoring (PCA/PLS), soft sensors/state estimation (Kalman filtering, moving-horizon estimation), real-time monitoring/model predictive control.
- Peer-reviewed publications; global/international team experience.
- Develop, calibrate, and validate bioreactor dynamic models to simulate, optimize, and control recombinant protein production and product quality attributes.
- Transfer and deploy bioreactor models in a commercial manufacturing environment to support process monitoring and forecasting (digital shadows) and/or real-time bioreactor optimization and control (digital twins).
- Work as part of matrix project teams with cell-culture development, process development, analytical, automation, and data science teams across R&D and manufacturing.
- Communicate model assumptions, uncertainty, limitations, and process interpretation to technical and non-technical stakeholders.
- Maintain reproducible computational workflows using coding, documentation, version control, and data-management practices.
- Act as a change agent to support the implementation of process modeling in established cell culture process development workflows.
- Document progress against deliverables via technical presentations, reports, and—when appropriate—scientific publications.
Qualifications
- Education: PhD in Chemical Engineering, Biochemical Engineering, Process Systems Engineering, Bioengineering, Biomedical Engineering, or related field; OR Master’s in the same fields.
- Experience: PhD—minimum 1 year relevant experience; Master’s—minimum 4 years relevant industry/research experience. Demonstrated experience developing dynamic models of bioreactor or cell culture processes.
- Skills: Proficiency in at least one of Python, MATLAB, Julia, or R.
Preferred Qualifications
- Dynamic mechanistic modeling using ODEs, kinetic rate laws, mass balances, parameter estimation, and model validation.
- Mammalian cell culture development (fed-batch and/or perfusion).
- Bioreactor digital twins/advanced monitoring tools in industrial biopharma.
- Bioreactor engineering principles (oxygen transfer, mixing, mass transfer, CO2 stripping, kLa, shear, scale-up/scale-down).
- Perfusion modeling (cell retention, bleed, media exchange, residence time, productivity, steady-state).
- Multivariate monitoring (PCA/PLS), soft sensors/state estimation (Kalman filtering, moving-horizon estimation), real-time monitoring/model predictive control.
- Peer-reviewed publications; global/international team experience.