Pharmacometrician - Principal Scientist in Drug Disease Modeling

Sanofi; Gentilly; Sur place; EUR 55 000 - 75 000; Plein temps

Résumé du poste

A leading pharmaceutical company in Île-de-France is seeking a highly talented Pharmacometrician to join their Drug Disease Modeling team. You will contribute to innovative modeling strategies and the application of methodologies across various therapeutic areas. The ideal candidate has 3+ years of experience in modeling and simulation, a PhD, and proficiency in languages like R and Python. Join us in advancing medical innovations.

Qualifications

• 3+ years in modeling and simulation within the pharmaceutical industry, CRO, or academia.
• Industry experience is highly preferred.
• Fluent spoken and written English; knowledge of French is a plus.

Responsabilités

• Develop and implement multiscale computational drug disease models.
• Apply innovative quantitative methods to facilitate decision-making.
• Collaborate with clinical pharmacology and therapeutic area teams.

Connaissances

Modeling and simulation

Quantitative clinical pharmacology

Proficiency in R

Proficiency in Python

Excellent communication skills

Team-oriented mindset

Experience with machine learning

Formation

PhD in pharmacometrics, biostatistics, or applied mathematics

Outils

Monolix

NONMEM

Julia

Matlab

Description du poste

Job Title:

The Disease Modeling department at Sanofi is seeking a highly talented and motivated Pharmacometrician to join our dynamic team and contribute to the advancement of Sanofi R&D portfolio. This group is part of the Quantitative Pharmacology within the Translation Medicine Unit at Sanofi R&D. Modeling and simulation play an increasingly important role at Sanofi, supporting critical decision-making throughout various phases of drug research and development.

Are you ready to shape the future of medicine? The race is on to speed up drug discovery and development to find answers for patients and their families. Your skills could be critical in helping our teams accelerate progress.

Join our Drug Disease Modeling (DDM) team as a Pharmacometrician, where you will support Sanofi’s R&D portfolio with innovative modeling strategies and drive the development and application of cutting-edge DDM methodologies.

Main Responsibilities:

1. Develop and implement multiscale computational drug disease models across therapeutic areas such as oncology, neurology, immunology, and inflammation.
2. Apply innovative quantitative methods to integrate pharmacokinetics, biomarkers, clinical outcomes, patient characteristics, and disease mechanisms to facilitate decision-making.
3. Design DDM modeling and simulation strategies to support research and development programs.
4. Conduct data analysis, simulation, interpretation of results, and reporting in compliance with GCP and regulatory guidance.
5. Collaborate with clinical pharmacology, biostatistics, translational medicine, and therapeutic area teams.
6. Communicate modeling results effectively to both technical and non-technical stakeholders.
7. Maintain expertise through continuous education, conferences, and workshops.

Experience: 3+ years in modeling and simulation within the pharmaceutical industry, CRO, or academia. Industry experience is highly preferred.

Skills: Broad knowledge of quantitative clinical pharmacology (PK/PD, statistics, systems pharmacology), proficiency in programming languages (R, Matlab, Python), experience with modeling tools (Monolix, NONMEM, Julia), excellent communication skills, and a team-oriented mindset. Experience with machine learning or deep learning is a plus.

Education: PhD in pharmacometrics, biostatistics, applied mathematics, or related field.

Languages: Fluent spoken and written English; knowledge of French is a plus.

Join us in pursuing progress and discovering extraordinary innovations in medicine. At Sanofi, we are committed to equal opportunities for all, regardless of race, color, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, or gender identity.

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