Principal Scientist, Quantitative Systems Pharmacology
Sanofi
Summary
Join our Quantitative Systems Pharmacology (QSP) group as a Principal Scientist and you'll support our R&D portfolio with mechanistic disease modeling.
Job description
Main Responsibilities
- Function as part of a team to develop and apply multiscale computational models of complex diseases based on detailed biological mechanistic knowledge to support model-informed drug discovery & development.
- Survey literature to gain understanding of critical physiological processes to be represented, and identify suitable mechanistic elements, data, parameters and assumptions to be included.
- Identify and analyze suitable preclinical and clinical data to inform modeling activities.
- Analyze simulation results and identify appropriate strategies to resolve issues pertaining to model performance and accuracy.
- Maintain extensive documentation of model development process, and data analysis.
- Maintain quantitative systems pharmacology expertise through comprehensive education
- Communicate modeling predictions to key stakeholders.
Basic Qualifications
- PhD or Master's degree in systems pharmacology, biology, computational biology, systems biology, biomedical engineering, or a related field.
- For candidates with PhD degrees: Minimum 2 years of hands-on experience generating disease models, mechanistic/multiscale models or QSP models. Academic research experience, internships and post-doctoral fellows will be considered.
- For candidates with Master degrees: Minimum 4 years of experience generating disease models, mechanistic/multiscale models or QSP models in industry/pharma setting.
Preferred Qualification
- Proficiency in mathematical modeling using differential equations.
- Machine learning experience.
- Extensive experience in computational tools such as MATLAB, Julia and R (MATLAB preferred).
- Excellent oral and written communication skills.
- Ability to quickly learn pathophysiology in indications of interest.
- Highly motivated, detail-oriented, and independent researcher.
- Able to thrive in a collaborative, multi-disciplinary team environment.
- Strong commitment to on-the-job-training.
- Knowledge of Immuno-oncology or Neuroscience.
- Record of increasing responsibility and independence, developing and applying QSP approaches.
- Experience in drug discovery/development a plus, especially in pharmacometrics.
Requirements
- PhD or Master's degree in systems pharmacology, biology, computational biology, systems biology, biomedical engineering, or a related field.
- For candidates with PhD degrees: Minimum 2 years of hands-on experience generating disease models, mechanistic/multiscale models or QSP models. Academic research experience, internships and post-doctoral fellows will be considered.
- For candidates with Master degrees: Minimum 4 years of experience generating disease models, mechanistic/multiscale models or QSP models in industry/pharma setting.
Benefits
- Employee benefit programs
Company overview
Sanofi is a French multinational pharmaceutical and healthcare company headquartered in Paris, France. Founded in 1973, the company has grown through strategic mergers and acquisitions to become one of the world's leading pharmaceutical corporations. Sanofi operates as an R&D-driven, AI-powered biopharma company committed to improving people's lives through breakthrough science. The company's portfolio spans seven major therapeutic areas including cardiovascular diseases, diabetes, oncology, immunology, rare diseases, neurology, and vaccines. Through its subsidiary Sanofi Pasteur, it is the world's largest producer of vaccines. Sanofi employs over 84,000 people across more than 100 countries and maintains a significant presence in both developed and emerging markets. The company's innovative pipeline focuses on advancing precision medicines and biologics while maintaining a strong commitment to sustainability and ethical business practices. Sanofi's mission centers on chasing the miracles of science to deliver transformative healthcare solutions globally.