About the position
Quantitative Systems Pharmacology (QSP) Modeler will serve as the QSP lead on a number of pre-clinical and clinical development programs. The individual will oversee all aspects of QSP strategies for candidate drug products from early development through late stage development using model-based approaches to improve the efficiency of drug development, and improve our mechanistic understanding, and to support dose selection of clinical candidates. This position's primary role is to develop and implement QSP models, supporting the development of novel therapies including antibody-drug conjugates (ADC), bispecific antibodies, immuno-oncology agents, and other mechanisms. The successful candidate will collaborate with discovery, preclinical, translational and clinical development as well as other scientists in the Translational and Quantitative Sciences group to develop mathematical models and help understand targeted biological pathways and interactions of novel therapeutic modalities. The candidate is responsible for framing critical questions to establish the right modeling & simulation strategies that enable lead optimization, identify PK/PD relationships, inform dose selection and Go/No Go decisions by utilizing mechanistic QSP models. Essential qualifications include in-depth understanding of cell biology – particularly in immunology and oncology – and numerical methods, as well as hands-on experience with modeling software, ability to clearly present modeling and simulation findings, and demonstrate ability to thrive in a matrix environment working at the leading edge of technologies. The candidate will design and build models based on preclinical and emerging clinical data as well as leveraging literature sources of data and relevant immuno-oncology and oncology knowledge. The candidate will cultivate data in support of model construction and interpretation, define key issues, and provide simulations of disease, mechanism of action, and (non)clinical studies. The candidate should be driven to use all tools at their disposal (QSP, PK/PD, Machine Learning (ML) and Artificial Intelligence (AI)) to understand the clinical pharmacokinetics and pharmacodynamics of novel drug candidates. The candidate will contribute to best practices on application of QSP and other mathematical or statistical analyses (e.g. artificial intelligence, machine learning, deep learning) across the clinical pharmacology group. This is an exciting opportunity to be part of a passionate, high profile, high-impact Clinical Pharmacology team, and work in a highly dynamic and collaborative setting.
Responsibilities
- Serve as the QSP lead on pre-clinical and clinical development programs.
- Oversee QSP strategies for candidate drug products from early to late stage development.
- Develop and implement QSP models for novel therapies including ADCs and bispecific antibodies.
- Collaborate with various teams to develop mathematical models and understand biological pathways.
- Frame critical questions to establish modeling & simulation strategies.
- Identify PK/PD relationships and inform dose selection using mechanistic QSP models.
- Design and build models based on preclinical and clinical data.
- Cultivate data for model construction and interpretation.
- Provide simulations of disease and mechanism of action.
- Contribute to best practices on application of QSP and statistical analyses.
Requirements
- Ph.D/Pharm.D/M.D with training in relevant fields.
- 2-5 years of industry and/or academic experience in mathematical modeling of biological systems.
- Strong competency in modeling and simulation software such as Matlab/SimBiology or Julia.
- Proficiency with systems modeling approaches including differential equations and agent-based modeling.
- Understanding of PK/PD analysis and translational modeling of preclinical data.
- Demonstrated ability to apply modeling and simulation approaches in drug development.
- Ability to present results at cross-functional teams and conferences.
- Extensive experience in oncology drug development is preferred.
- Experience in Machine Learning/Artificial Intelligence is preferred.
- Excellent written, verbal, and interpersonal communication skills.
Nice-to-haves
- Experience in a fast-growing, dynamic company.
- Positive attitude and ability to work with multidisciplinary teams.
Benefits
- 401(k) Plan: 100% match on the first 6% of contributions.
- Health Benefits: Two medical plan options, dental, and vision insurance.
- Voluntary Plans: Critical illness, accident, and hospital indemnity insurance.
- Paid vacation, sick leave, holidays, and 12 weeks of discretionary paid parental leave.
- Access to child and adult backup care, family support programs, financial wellness tools, and emotional well-being support.
- Commuter benefits, tuition reimbursement, and a Lifestyle Spending Account for wellness and personal expenses.
