Purpose:

The QSP Senior Scientist will lead and apply mechanism-based PK/PD and Quantitative Systems Pharmacology (QSP) modeling to support translational and clinical drug development across multiple therapeutic areas. The role integrates modeling, simulation, and data-driven insights to guide dose selection, study design, and strategic decision-making across early and late-stage programs.

You will be responsible for:

Scientific Leadership & Modeling Excellence

• Lead development and application of QSP, PBPK, and PK/PD models to address critical drug discovery and development questions across stages
• Design and execute population PK/PD and mechanistic modeling strategies to inform clinical pharmacology plans and regulatory submissions
• Conduct exploratory modeling analyses with rapid turnaround to support high-impact decisions
• Demonstrate strong scientific rigor in model development, validation, interpretation, and communication of results

Translational Impact & Development Strategy

• Drive model-informed decision-making for study design, dose optimization (e.g., RP2D), and indication prioritization
• Contribute to clinical development strategies, including protocol design, modeling components in Phase 2/3 studies, and regulatory responses
• Provide mechanistic insights linking exposure-response relationships to clinical outcomes and formulation strategies

Emerging Modalities & Platform Innovation

The role will contribute to expanding QSP capabilities across advanced and complex biologics modalities, including:

• Antibody-drug conjugates (ADCs) and next-generation conjugate platforms
• Bispecific and multispecific T-cell engagers and immune-targeting biologics
• Cell therapies (including ex vivo and in vivo approaches)
• Targeted protein degradation and novel platform therapeutics

• Supporting dose strategy, early asset evaluation, and platform modeling frameworks. Developing modeling approaches for next-generation therapeutic modalities and applying innovative computational approaches, including AI/ML-enabled workflows, to enhance modeling scalability and insight generation

Collaboration & Cross-Functional Leadership

• Serve as a key modeling representative on cross-functional teams spanning clinical pharmacology, translational medicine, discovery, and clinical development
• Partner with internal and external stakeholders, including CRO oversight and collaboration
• Provide scientific mentorship and technical guidance to junior scientists

Scientific Contribution & Innovation

• Contribute to methodological innovation and model-informed drug development (MIDD) strategies
• Establish scientific credibility through publications, presentations, and external engagement
• Advance internal modeling platforms and best practices to improve reproducibility and efficiency

Requirements/Qualifications:

Education:

• Ph.D. degree in Pharmaceutical Sciences, Systems Biology/Pharmacology, Mathematics, Engineering, or other disciplines related to quantitative pharmacology or equivalent, or expecting a Ph.D. degree in 3 months.

Required:

• Experience in using quantitative approach in describing biological systems and demonstrated ability to apply quantitative tools to enable rational and efficient drug development
• Excellent understanding of theory, principles, and statistical aspects of advanced mathematical modeling and simulation, including numerical methods, parameter estimation/optimization, and ordinary differential equations (ODEs), and how these can be applied in the development of complex models of biological pathways and systems
• Proficient in Matlab/Simbiology
• Self-motivated team player who excels in a collaborative and multi-disciplinary team environment
• Good oral and written communication skills and the ability to interact effectively with scientists in other disciplines with a positive, collegial and collaborative attitude.

Preferred:

• Experience in building, validating and using QSP models or deterministic models of biological pathways/systems to support basic science and/or translational clinical research strongly preferred
• Good understanding of the basic principles of pharmacokinetics and pharmacodynamics for conventional large and small molecule therapeutics as well as novel therapeutic modalities
• Familiarity with AI/ML-enabled modeling workflows or large-scale data integration approaches
• Experience with general programming and data analysis tools/languages such as R would be particularly desirable
• Strong publication records highly desirable
• Strong collaboration and communication skills in multidisciplinary environments
• Ability to independently lead projects while influencing broader team strategy
• Scientific curiosity and commitment to continuous innovation and learning

Required Skills:

Preferred Skills: