Upstream Process Development, Scientist / Senior Scientist

Role Summary

We are advancing the science of animal health and seek a scientist or engineer who can run bioreactors, model approaches, and turn data into decisions. You will own study design with DoE, translate CFD into practical scaling and control, and explore multivariate analytics and pragmatic models to drive yields and process improvements.

Responsibilities

• Design, run, and interpret mammalian cell culture experiments in shake flasks and bioreactors for monoclonal antibodies and proteins
• Apply DoE, statistical models, and scale-up principles to optimize processes
• Design, execute, and translate CFD modeling into actionable scaling and control strategies
• Champion multivariate analyses and modeling (e.g., PCA, time-series analytics), and explore mechanistic models and hybrid/ML approaches where they add value
• Leverage digital tools and data systems to improve process understanding and decision-making
• Collaborate across upstream, downstream, formulation, and analytical teams
• Document work in electronic lab notebooks and author high-quality technical reports
• Support tech transfers and regulatory filings with clear, traceable documentation
• Drive innovation by evaluating new bioprocess technologies and modeling approaches to improve workflows

Qualifications

• Required: Degree in Biochemistry, Molecular Biology, Biotechnology, Chemical/Biological/Biomedical Engineering, or related field.
• Required: BS and 4-7+ years industry experience, or MS and 1-3+ years industry experience, or PhD with relevant research and/or 0-3+ years industry experience
• Preferred: Strong upstream bioprocess foundation: cell culture and bioreactor operation (fed-batch, perfusion)
• Preferred: Scale-up/scale-down fluency and mass transfer fundamentals
• Preferred: DoE and MVDA literacy; experience with statistical tools a plus
• Preferred: CFD literacy with applied impact; hands-on experience a plus
• Preferred: Familiarity with scientific computing and data analysis tools and languages; capacity to interpret and adopt quickly
• Preferred: Exposure to mechanistic/kinetic modeling (e.g. Monod growth kinetics) and practical ML
• Preferred: Clear, first-principles reasoning; can explain assumptions and design validation experiments
• Preferred: Success bridging bench science, process engineering, and data science; strong communication and organization