Scientist - Analytical and Purification Sciences

Location: Cambridge, United Kingdom

Company

IQVIA provides clinical research services, commercial insights, and healthcare intelligence for the life sciences and healthcare industries.

What you will do

• Deliver analytical and purification data supporting compound progression across multiple small-molecule drug discovery projects.
• Develop and optimise chromatographic methods for complex molecules, including scaling methods from analytical to preparative chromatography.
• Apply UPLC/HPLC, SFC, mass spectrometry, and NMR to solve analytical challenges.
• Innovate analytical and purification workflows through plate-based methods, NMR, AI, machine learning, and laboratory automation.
• Use data science to improve predictive capability, throughput, and decision-making while helping shape future departmental capabilities.
• Troubleshoot, maintain, and optimise instrumentation, and provide scientific guidance and supervision to junior colleagues.

Requirements

• Proven experience in achiral and chiral automated purification.
• Strong practical expertise in UPLC/HPLC, SFC, NMR, and mass spectrometry.
• Demonstrated experience in method development and chromatographic optimisation.
• Experience applying AI or machine learning in analytical workflows, data interpretation, or laboratory automation.
• Hands-on experience troubleshooting and maintaining analytical instrumentation.
• Excellent written and verbal communication skills, with a passion for mentoring junior scientists.

Nice to have

• Experience with MassLynx, ChemStation, Chromscope, or Trilution.
• A track record of implementing new technologies or creative approaches to improve performance and efficiency.

Culture & Benefits

• Collaborative, multidisciplinary environment within a global contract research organisation.
• Work at the forefront of small-molecule drug discovery and analytical science.
• Opportunity to contribute to AI, automation, and next-generation laboratory workflows.
• Role in accelerating data-driven decision-making and DMTA cycles.
• Career development in a scientifically ambitious environment.