Bioanalytical Lead (AI Drug Discovery)

Location: London, hybrid working with attendance at the office 3 days per week

Company

hirify.global develops AI models and computational drug design technologies to accelerate scientific discovery and the development of innovative medicines.

What you will do

• Define and execute bioanalytical strategies for small molecules, biologics, and antibody-drug conjugates across discovery and preclinical development.
• Oversee the design, validation, and transfer of PK, PD, immunogenicity, and biomarker assays at external laboratories.
• Collaborate with AI and machine learning scientists to integrate bioanalytical data into predictive models.
• Select, qualify, and manage global CRO partners, ensuring data quality, timelines, and budget alignment.
• Lead international sample shipment, cold-chain management, import/export compliance, and stability tracking.
• Represent bioanalysis in cross-functional teams and author or review bioanalytical sections of IND applications and Investigator Brochures.

Requirements

• Ph.D. in Biochemistry, Analytical Chemistry, Pharmacology, or a related discipline with 5+ years of biotech or pharmaceutical industry experience, or a B.S./M.S. with 8+ years of equivalent experience.
• Demonstrated leadership of bioanalytical strategies covering small molecules, biologics, and ADCs.
• Deep knowledge of GLP and non-GLP preclinical regulations, FDA and EMA bioanalytical method validation guidance, and ICH guidelines.
• Extensive experience with analytical platforms including LC-MS/MS, ligand-binding assays, and flow cytometry.
• Experience developing and validating biomarker assays, managing global sample logistics, and maintaining chain of custody.
• Experience writing bioanalytical sections for successful IND submissions.

Nice to have

• Experience generating reagents in collaboration with drug design teams.
• Experience developing or using AI tools, including LLMs, to accelerate reporting and related processes.

Culture & Benefits

• Hybrid working model designed to support knowledge sharing and collaboration.
• Collaborative, interdisciplinary environment spanning drug discovery, translational medicine, computational biology, and machine learning.
• Values centered on thoughtful, brave, determined, and collaborative scientific work.
• Commitment to equal employment opportunities and workplace accommodations.