At Eisai, satisfying unmet medical needs and increasing the benefits healthcare provides to patients, their families, and caregivers is Eisai’s human health care (hhc) mission. We’re a growing pharmaceutical company that is breaking through in neurology and oncology, with a strong emphasis on research and development. Our history includes the development of many innovative medicines, notably the discovery of the world's most widely-used treatment for Alzheimer’s disease. As we continue to expand, we are seeking highly-motivated individuals who want to work in a fast-paced environment and make a difference. If this is your profile, we want to hear from you.

The Director of Quantitative Systems Pharmacology (QSP) will lead the development and implementation of mechanistic knowledge of biology integrated into mathematical models that support drug discovery and clinical development. The Director of QSP will provide scientific leadership, define QSP strategies, and collaborate across Eisai’s Deep Human Biology Learning (DHBL) project teams to translate existing knowledge and data into actionable QSP models. These models will provide functional understanding of complex nonlinear pathophysiological systems and their interactions, will allow scientists to explore system dynamics and biological hypotheses, will yield insight into responses to different pharmacological approaches to modulating biological systems, and will provide mechanistic insights to inform Eisai’s R&D decisions (for example FIH translation, dose selection, candidate selection, selection of target population, and combination strategies). This Director QSP position is essential for supporting the increasing expectations of team leaders and global regulators of applying QSP methodologies to continuously improve the conceptual and mechanistic understanding of the relations among drug exposure, efficacy, and safety.

Essential Functions:

Develop, implement, and apply QSP models to understand diseases, their pathways, and their progressions. Evaluate DHBL drug candidates and treatment modalities to predict their effects and optimize therapeutic strategies (e.g., the selection of target tumor types and populations), and to support clinical introduction including first-in-human dose selection.
Use and improve existing Neurology QSP Platforms to gain insights into the causal relationships between biological and drug-level responses, enhancing the understanding of drug-target interactions and disease mechanisms. Develop new QSP models and platforms as they are needed.
Lead DHBL preclinical and early clinical QSP development strategy to support optimal dose selection with simulations based on mechanistic understanding to assess efficacy and safety.
Advocate for model-informed drug discovery and development (MIDD) approaches. Provide scientific leadership, present research at scientific conferences, and integrate MIDD strategies into Eisai’s R&D programs to improve efficiency and decision-making.
Foster collaboration across functional groups and promote the development of new modeling tools and methods. Advance the adoption of QSP capabilities to improve the predictive power of these tools to support the design and optimization of drug combinations.
Manage the expectations/timelines of assigned M&S work for the relevant project teams.
Use mathematical, computational, and statistical tools to analyze and interpret large, complex data sets to gain insights into the causal relationships between drug-target interactions and disease mechanisms.
Provide scientific/strategic expertise across multiple therapeutic areas to support decision-making in the conversion of discovery to clinical through the design, development, and execution of quantitative mechanistic models to support the translational process.

Requirements:

PhD, MD-PhD and/or PharmD in Bioengineering, Systems Biology, Applied Mathematics, Computational Biology, Pharmacometrics, Chemical Engineering, Pharmaceutical Sciences, or a related quantitative discipline.
Deep knowledge of the principles and theoretical aspects of applied mathematical modeling, including numerical methods, ordinary differential equations (ODEs), partial differential equations (PDEs), parameter estimation/optimization, and how these tools can be applied in the development of complex biochemical models.
Deep expertise in quantitative systems pharmacology (QSP), mechanistic modeling, systems biology, and translational science.
Demonstrated experience applying QSP approaches to support drug discovery and/or clinical development programs with measurable program impact.
Strong understanding of pharmacology, physiology, immunology, molecular biology, and disease mechanisms.
Ability to learn quickly in new areas of biology and to use a solid foundation of quantitative skills to apply newly acquired knowledge to build mechanistically sound QSP models.

Eisai Salary Transparency Language:

The annual base salary range for the Director, Quantitative Systems Pharmacology is from :$208,200-$273,200

Under current guidelines, this position is eligible to participate in : Eisai Inc. Annual Incentive Plan & Eisai Inc. Long Term Incentive Plan.

Final pay determinations will depend on various factors including but not limited to experience level, education, knowledge, and skills.

Employees are eligible to participate in Company employee benefit programs. For additional information on Company employee benefits programs, visit https://careers.eisai.com/us/en/compensation-and-benefits.

Certain other benefits may be available for this position, please discuss any questions with your recruiter.

Eisai is an equal opportunity employer and as such, is committed in policy and in practice to recruit, hire, train, and promote in all job qualifications without regard to race, color, religion, gender, age, national origin, citizenship status, marital status, sexual orientation, gender identity, disability or veteran status. Similarly, considering the need for reasonable accommodations, Eisai prohibits discrimination against persons because of disability, including disabled veterans.

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