PKPD & AI Modelling Lead, Quantitative Pharmacology

PKPD Modelling Lead / Senior Quantitative Pharmacology Scientist

Location:

London, UK OR Lausanne, CH

Working Pattern:
Hybrid, three days on-site

A cutting-edge AI-driven drug discovery organisation is expanding its quantitative pharmacology group in London. Backed by major investment and powered by world-class machine learning and computational tools, the company brings together drug discovery, biology, PKPD and ML engineering to design medicines in a way traditional pharma can’t match. Their platform uses advanced predictive and generative AI to model biology, guide design decisions and accelerate the move from target to clinical candidate.

They operate with the pace of a tech company and the scientific depth of a serious research organisation, scaling quickly across multiple therapeutic areas and modalities.

They are hiring a PKPD Modelling Lead to drive quantitative pharmacology strategy across biologics and small molecule programs. This is a high-impact scientific role that sits at the centre of discovery, data science and AI-enabled design. You will work closely with ML engineers and computational scientists to help shape the models that inform dose predictions, exposure, safety margins and modality selection.

Your modelling input feeds directly into their AI drug design engine, making PKPD a core part of how decisions are made.

• Lead PK, PD and modelling strategy across discovery programs
• Build mechanistic and empirical PKPD models to support dose, exposure and safety predictions
• Shape early target molecule profiles and guide modality selection
• Integrate PK, PD and tox data into AI-driven decision frameworks
• Work daily with ML engineers, computational biology, drug design, DMPK and pharmacology
• Contribute to regulatory writing for IND/IMPD and early clinical packages
• Oversee CRO interactions and outsourced data generation

• PhD in a relevant scientific field
• 5+ years of industry experience in PKPD, quantitative pharmacology or related areas
• Strong PKPD modelling experience for biologics
• Hands‑on experience developing or interpreting mechanistic models
• Experience generating PKPD or quantitative packages for early development
• Exposure to ML‑enabled workflows, AI‑first environments or computational teams is beneficial
• Familiarity with Python or KNIME is an advantage

• Competitive salary and strong benefits package
• Work alongside leading figures in AI, computational biology and drug discovery
• A high-impact scientific role at the heart of AI‑first drug design
• A culture built on collaboration, curiosity and ambitious science