Job Specifications
At Genmab, we are dedicated to building extra[not]ordinary(r) futures, together, by developing antibody products and groundbreaking, knock-your-socks-off KYSO antibody medicines(r) that change lives and the future of cancer treatment and serious diseases. We strive to create, champion and maintain a global workplace where individuals' unique contributions are valued and drive innovative solutions to meet the needs of our patients, care partners, families and employees.
Our people are compassionate, candid, and purposeful, and our business is innovative and rooted in science. We believe that being proudly authentic and determined to be our best is essential to fulfilling our purpose. Yes, our work is incredibly serious and impactful, but we have big ambitions, bring a ton of care to pursuing them, and have a lot of fun while doing so.
Does this inspire you and feel like a fit? Then we would love to have you join us!
The Role
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.
Skills & Experience
Ph.D/Pharm.D/M.D with training in chemical or biomedical engineering, immunology, pharmaceutical sciences, mathematics, statistics, or equivalent area with 2-5 years of industry and/or academic experience in mathematical modeling of biological systems. Job title is flexible based on experience.
Strong competency in applying modeling and simulation related software such as Matlab/SimBiology, Julia, or other domain-specific languages is required.
Proficiency with systems modeling approaches such as ordinary, partial, and/or stochastic differential equations, boolean, agent-based, or other advanced mechanistic modeling approaches is essential.
Understanding of PK/PD analysis and translational modeling of preclinical PK/PD data, and mechanism-based PK/PD systems using preclinical and/ or clinical data with biologics therapeutics is also required
Demonstrated ability and experience in applying modeling and simulation approaches to enable rational and efficient preclinical and clinical drug development are required
Demonstrated ability to present results at cross-functional teams, department meetings, review committees, and conferences.
Extensive experience and strong understanding of oncology drug development is preferred
Experience in Machine Learning/Artificial Intelligence is preferred
Flexible, with positive attitude, ability to work with multidisciplinary teams, prioritize projects effectively an
About the Company
We are an international biotech company committed to our goal of improving the lives of patients through innovative and differentiated antibody therapeutics. For 25 years, our passionate, innovative and collaborative team has invented next-generation antibody technology platforms and leveraged translational, quantitative, and data sciences, which has resulted in a proprietary pipeline including bispecific T-cell engagers, antibody-drug conjugates, next-generation immune checkpoint modulators, and effector function enhanced a...
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