Pharmacometrics Lead AI/Machine Learning

Pharmacometrics Lead AI/Machine Learning

UCB - Union Chimique Belge

Slough, Monheim or Braine

About the role

Developing, implementing and leading Pharmacometrics AI/ML strategy to support development of our assets. Extrapolation (where appropriate), dose selection, trial design, PIP development and ongoing evaluation of data as generated.

Who you’ll work with

Reporting to Global Head of Pharmacometrics.

What you’ll do

• Evaluating and implementing emerging technologies for quantitative clinical pharmacology and pharmacometrics applications.
• Performing hands-on AI/ML, population PK/PD, exposure-response modeling and clinical trial simulations on an assigned project.
• Developing submission ready fit for purpose Pharmacometrics analysis plans and reports.
• Initiating and overseeing outsourced pharmacometrics and AI/ML activities and further internalizing the work as needed.
• Supporting the development of a team from both a technical and scientific perspective to ensure state of the art pharmacometrics approaches are applied to optimize drug discovery and development.
• Having a working knowledge about the regulatory pathways on a global basis and opportunities for collaboration with health authorities (either through working parties, advice meetings, and other initiatives) in Paediatrics, Neurology and Immunology.
• Working closely with the QCP Leads assigned to the various projects to ensure clear integration of the pharmacometrics aspects into the programs.
• Promoting MID3 internally and externally.

Interested? For this role you'll need the following education, experience and skills

• Doctorate (PhD or MD) in the related disciplines of pharmaceutical sciences, statistics or engineering.
• 8+ years (Bio)pharmaceutical industry experience in pharmacometrics with at least 2 years of experience in the applications of AI/ML in drug development.
• Hands-on experience in population PK/PD, disease progression modeling, and clinical trial simulations.
• Strong understanding and knowledge of Bayesian methodologies, and model-based meta-analyses.
• Hands on Expertise in R, Python, Matlab, NONMEM or related pharmacometrics software.
• Agile mindset with a proven ability to adjust by prioritizing multiple tasks.
• Outstanding communication, organizational and presentation skills.