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AVP​/Head, Quantitative Pharmacology & Pharmacometrics - Oncology

Job in Rahway, Union County, New Jersey, 07065, USA
Listing for: MSD Malaysia
Full Time position
Listed on 2026-09-24
Job specializations:
  • Science
    Pharmaceutical Science/ Research, Data Scientist
Salary/Wage Range or Industry Benchmark: 282200 - 444200 USD Yearly USD 282200.00 444200.00 YEAR
Job Description & How to Apply Below
Position: AVP/ Head, Quantitative Pharmacology & Pharmacometrics - Oncology

Our company is seeking a head of Quantitative Pharmacology & Pharmacometrics (QP2) to lead pharmacology strategy for our oncology pipeline. Our QP2 organization uses trial and external data, advanced analytics, model-informed drug development, and deep therapeutic-area expertise to inform the safety, efficacy, dose, schedule, patient‑selection, and benefit-risk strategies for the pipeline. The QP2 team integrates clinical pharmacology, pharmacometrics, quantitative systems pharmacology, exposure-response analysis, translational modeling, biomarkers, real-world data, and emerging AI/ML-enabled approaches to support decision‑making across discovery, early clinical development, registration, and life‑cycle management.

The Head of QP2 Oncology will serve as the enterprise point of accountability for QP2 strategy, ensuring quantitative science is used to shape program design, dose and regimen strategy, regulatory interactions, governance recommendations, and portfolio decisions. The leader will provide strategic leadership and scientific oversight, supervise and mentor other scientists, directly shape strategy for priority programs and business‑development opportunities, and build a modern quantitative operating model for oncology development.

Education
  • Ph.D. or equivalent degree

At least 15 years of experience in drug development, including at least 5 years in oncology in industry educational background in biopharmaceutics, pharmaceutical sciences, pharmacometrics, mathematics, statistics/biostatistics, computational biology/chemistry, chemical/biomedical engineering, or a related field.

Required Experience and Skills
  • Drug development expertise within the oncology therapeutic area, including a record of both internal and external impact in driving model‑informed drug development strategies
  • Experience with multiple modalities such as ADCs, small molecules, TCEs and/or biologics
  • Track record of publications or presentations in quantitative pharmacology for oncology
  • Ability to influence regulatory strategies including experience independently authoring and defending regulatory filings for marketing authorization (NDA/MAA) for more than one modality (biologics, ADCs, small molecules, cell therapy)
  • Demonstrated ability to participate in and to lead an interdisciplinary team, and to oversee the work of other scientists
  • Experience in performing population PK/PKPD analyses using standard pharmacometric software
  • Up to date on emerging trends/works in field (peer intelligence, methodology emerging analyses Scientific understanding of biopharmaceutical and ADME properties of both small molecules and biologics
  • Proficiency in written and verbal communication, interdisciplinary collaboration, and problem scoping and planning
  • Ability to formulate and articulate scientific strategy to support portfolio decisions
  • Ability to embed MIDD early in asset strategy, including population PK, PK/PD, exposure‑response, dose optimization, simulation‑based trial design, model‑based meta‑analysis, quantitative systems pharmacology, disease‑progression modeling, and translational modeling
  • Ability to recommend and defend dose and regimen decisions supported by integrated evidence packages that address efficacy, safety, tolerability, exposure, biomarkers, patient selection, and regulatory expectations
  • Comprehensive understanding of global regulatory expectations for oncology development for dose and pharmacology; contributing to cross‑functional regulatory strategy; reviewing INDs, CSRs, CTDs, NDAs/BLAs/MAAs; and representing QP2 leadership at regulatory interactions
  • Willingness and experience to apply AI/ML, automation, real‑world data, biomarker, translational, and digital data‑stream approaches where they improve prediction,…
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