Associate Director Quantitative Data Modeling

*
* Job Title:

** Associate Director Quantitative Data Modeling
*
* Location:

** Morristown, NJ
** About the Job
** The Quantitative Pharmacology (QP) organization at Sanofi is dedicated to ensuring the safe and effective therapeutic treatment of patients by applying sound pharmacokinetic (PK) and pharmacodynamic (PD) principles to accelerate and optimize drug development.

Within QP, the Pharmacometrics group employs Model-Informed Drug Discovery and Development (MIDD) approaches to support decision making throughout the drug development continuum. This includes guiding dose selection and study design, de-risking potential drug–drug interactions, quantifying benefit–risk relationships, and contributing to regulatory submissions and interactions from first-in-human studies through life-cycle management.

This position, based in Morristown, NJ, requires the successful candidate to be onsite three days per week. The Associate Director will play a key role in advancing Sanofi’s internal pipeline across multiple therapeutic areas by conducting a broad spectrum of model-based analyses, spanning population PK and PK/PD modeling, disease progression modeling, and advanced methodologies such as data analytics, machine learning, and mechanistic modeling.

The incumbent will contribute to data-driven decision making and the application of innovative modeling strategies with minimal supervision.
*
* Main Responsibilities:

** Associate Director is expected to be equipped with multifaceted quantitative expertise applied to drug development including:
* Ability to formulate drug development questions and develop modeling and simulation strategies for optimizing model-informed drug development in all stages of development, ranging from first in human dosing through life cycle management.
* Conduct with minimal supervision model-based analyses such as population pharmacokinetics (PPK), PK/PD, exposure response, model-based meta-analysis, clinical trial simulations and disease progression modeling to drive internal pipeline forward.
* Conduct innovative quantitative methodologies such as data analytics, machine learning and mechanistic modeling as needed.
** About You
**** Required Experiences
*** Strong quantitative skills including but not limited to mathematics, statistics, data science, AI/ML, etc.
* Deep and extensive experience with advanced population modeling approaches (e.g. PPK, PKPD, etc. ) and complex methodologies such data analytic, AI/ML and mechanistic modeling.
* Proficiency in conducting model-based analyses using standard pharmacometrics software (e.g., NONMEM, R, Monolix, etc.)
* Proficiency in integrating and analyzing internal and external data to support decision making and to generate scientific insights.
* Strong written and verbal communication skills, interdisciplinary collaboration, problem scoping and planning.
* Good knowledge of industry best practices, global regulatory, processes, standards of drug development.
** Demonstrate the following scientific competencies:
*** Serve as M&S representative for clinical development projects.
* Act as a subject matter expert addressing technical and theoretical issues in the M&S area.
* Be able to operate in a multicultural environment and participate in international teams.
* Prepare and issue stand-alone M&S reports with minimal review.
* Summarize documents for submission to regulatory agencies, including but not limited to the FDA and EMA
* Prepare abstracts, posters and manuscripts for external presentation and/or publication.
* Present at external scientific meetings as appropriate
* Identify skill-sets necessary to advance career development.
* Ensure that all assigned project activities are performed in compliance with current departmental SOP’s, guidelines, industry best practices and regulatory guidelines, and are conducted following acceptable scientific rationale.
** Qualifications
* ** Ph.D. with or without post-doctoral experience in pharmacometrics, biopharmaceutics, pharmaceutical sciences, mathematics, statistics, data science, computational biology, chemical/biomedical engineering, system pharmacology, computer science or closely related discipline plus…