Manager / Associate Director, MIDD AI/ML Scientist

Role Summary

The Manager / Associate Director, MIDD AI/ML Scientist will focus on the practical application of emerging AI/ML and hybrid AI/ML-Pharmacometrics methodologies to projects on an asset and disease level supporting a wide range of clinical development decisions across therapeutic areas. The role will consist of collaborating with Clinical Pharmacology leads and cross-functional teams to integrate diverse sets of biomedical data into hybrid AI/ML-Pharmacometrics approaches, including analysis planning, execution, interpretation and communication of results. This position is an excellent entry point for quantitative scientists with a background in AI/ML looking to develop expertise at the AI/ML-pharmacometrics interface and apply those techniques to benefit patients at the frontier of medicine.

Responsibilities

• Execute portfolio projects: Apply AI/ML and hybrid AI/ML-pharmacometrics approaches to deliver analyses that inform drug development decisions (dose, regimen, endpoint, population, trial design).
• Work cross-functionally: Collaborate with CPMS leads, QSP modellers, statisticians, and digital/imaging experts to integrate AI/ML analyses with existing modelling frameworks. This will cover developing analysis plans, conducting analyses and communicating interim/final results.
• Drive stakeholder engagement: Proactively interact with stakeholders, line and middle management, staff and external contacts on a functional and tactical level.
• Contribute to our capabilities: Engage with academic groups and external vendors, supporting joint projects and learning cutting-edge methods. Identify best practices, trends, learnings, etc from internal and external sources
• Drive awareness: Contribute to training, seminars, and internal communications to increase literacy in AI/ML-pharmacometrics.
• Have external influence: Conference presentations, posters, and publications in scientific journals to represent GSK’s impact in AI/ML for drug development.

Qualifications

• Required: PhD (or equivalent) in AI/ML or related quantitative fields.
• Required: Strong foundation in AI/ML, statistics or pharmacometrics / quantitative clinical pharmacology.
• Required: Experience coding AI/ML pipelines in Python/PyTorch or Julia/Pumas.
• Required: Strong interest in developing hybrid AI/ML-pharmacometrics expertise.
• Required: Excellent collaboration and communication skills (written, verbal and presentation), with the ability to work in interdisciplinary teams.

Education

• PhD (or equivalent) in AI/ML or related quantitative fields.