Associate Principal Scientist

Overview

As an Associate Principal Scientist at Lonza, you will lead strategic scientific projects to advance biopharmaceutical process development and optimization. You collaborate across manufacturing, quality control, and regulatory affairs to translate insights into scalable processes. You will design experiments, interpret data, and mentor junior staff while staying ahead of industry advances. This role offers an opportunity to shape manufacturing innovations within a global, inclusive R&D environment.

Responsibilities

• Lead and manage scientific projects focused on upstream and downstream biologics processing
• Design, execute, and analyze experiments to improve process understanding, efficiency, yield, and quality
• Author and review technical reports, protocols, and regulatory documents supporting process development and manufacturing
• Provide scientific leadership and mentorship to junior scientists and research associates
• Collaborate with cross-functional teams (manufacturing, quality control, regulatory affairs) to ensure project execution
• Stay current with industry trends and disseminate findings through internal and external presentations

Key requirements

• Ph.D. in relevant field with 5+ years of biopharma industry experience or Master’s with 10+ years
• Experience with vector and/or host cell engineering to integrate payloads and/or knockout gene function in mammalian cells
• Proven track record leading successful scientific projects in biopharmaceutical process development
• Deep knowledge of upstream (cell culture, fermentation) and/or downstream (chromatography, filtration) processing
• Strong expertise in Design of Experiments (DOE), statistical analysis, and data interpretation
• Excellent written and verbal communication; ability to present complex data effectively
• Ability to work independently, manage multiple priorities, and thrive in a fast-paced environment

• strong communication
• independence and initiative
• cross-functional collaboration
• upstream/downstream bioprocessing
• Design of Experiments (DOE)
• statistical analysis

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