Director, Quantitative Pharmacology and Pharmacometrics – Oncology

About the role

The Quantitative Pharmacology and Pharmacometrics – Immune/Oncology (QP2-IO) team is part of the Global Clinical Development organization and oversees drug development aspects related to clinical pharmacology and pharmacometrics of oncology drugs from post-PCC to registration.

Responsibilities

• Serving as an expert representative for QP2 -IO on Oncology development teams.
• Framing critical questions and strategy for optimizing model-based analyses on programs.
• Developing and executing model-based analyses including translational PK/PD approaches, population pharmacokinetic models, exposure-response models, clinical trial design simulation, disease progression models, quantitative systems pharmacology (QSP) modeling, and comparator modeling.
• Strategizing and executing modeling of tumor size and survival.
• AMaintaining a comprehensive understanding of global regulatory expectations for small molecules and biologics in Oncology, authoring regulatory documents (investigational new drugs/INDs, clinical study reports (CSRs), clinical trial applications (CTDs), and representing QP2-IO at regulatory meetings.
• Mentoring and/or supervising junior staff to perform the above duties and to develop the above capabilities.

Requirements

• Minimum Education Required: Ph.D. with at least seven years of pharmaceutical drug development experience relating to: PKPD, pharmacometrics, mathematics, statistics/ biostatistics, or chemical/biomedical engineering.
• Masters or PharmD, with at least nine years of experience, where “experience” means having a record of increasing responsibility and independence in a similar role in pharmaceutical drug development.

Skills

• Demonstrated impact with applications of pharmacometrics methods.
• Experience in IND, NDA and other submissions to global regulatory agencies.
• Skills in experimental design, mathematical problem solving, critical data analysis/interpretation, and statistics.
• An exemplary record of increasing responsibility, independence, and demonstrated impact in driving drug development decisions through application of model-based approaches.
• Proficiency in R, NONMEM, MATLAB, Monolix or other modeling software.
• Professional working proficiency in written and verbal communication.