Company is searching for a creative, resourceful, integrative thinker who is driven to use cutting edge modeling approaches to advance the clinical and translational research in the Neurology Business Group . This individual will provide scientific leadership to develop and execute impactful Pharmacometric strategies in support of key decisions for programs across all phases of drug development. The complexity and scale of data being generated has grown significantly and requires strong scientific contributions to integrate different approaches in modeling, statistics and data science to bring value to the Company portfolio. This role requires diverse modeling experience in drug development within pharma/biotech, agility in being able to handle both hands-on modeling and strategic leadership for a dynamic project portfolio and a strong track-record of scientific and collaborative leadership in complex projects and initiatives.
Job Responsibilities:
Develop and execute mechanistic PK/PD models of neurodegenerative diseases, including physiologically-based pharmacokinetic (PBPK), quantitative system pharmacology (QSP) models, clinical trial simulations, literature meta-analysis, machine-learning/deep learning and other state of the art quantitative techniques.
Responsible for In-depth modeling
Represent Pharmacometrics and effectively communicate Pharmacometric input to key management and decision boards.
Develop Pharmacometrics strategies for Translational Medicine (in particular using continuous, longitudinal biomarkers) and various big, digital, historical data groups and initiatives.
Qualifications:
PhD or PharmD in Pharmaceutical Sciences, Clinical Pharmacology, Applied Mathematics, Engineering or related area with strong background in the application of mathematical and statistical methods with 5+ years of experience
Proven leadership skills managing complex technical and scientific team members, providing support within the disease area and increasing their effectiveness and expertise by coaching and mentoring.
In-depth modeling skills with the ability to translate complex problems into incisive models.
Proficiency in written and verbal communication, interdisciplinary collaboration, and problem scoping and planning.
Experience and disease knowledge in neuroscience is preferable.