Upstream Process Development, Scientist / Senior Scientist
Zoetis
6 months ago
On-site
Kalamazoo, MI
Operations
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
- Lead multivariate analyses and modeling (e.g., PCA, time‑series analytics); develop and deploy reproducible analyses (notebooks/scripts) that inform decisions
- Build pragmatic models (mechanistic or hybrid/ML where valuable) and validate with targeted experiments; document assumptions, limits, and outcomes
- Leverage digital tools and data systems to improve process understanding and decision‑making; ensure data provenance and version control
- Translate CFD modeling into actionable scaling and control strategies
- Collaborate across upstream, downstream, formulation, and analytical teams to align priorities and move decisions forward
- Document work in electronic lab notebooks and author high‑quality technical reports and decision memos
- Support tech transfers and regulatory filings with clear, traceable documentation
- Drive innovation by evaluating new bioprocess technologies and modeling approaches; create reusable templates and analysis pipelines to raise team velocity
Basic Qualifications:
- Bachelor’s degree in Biochemistry, Molecular Biology, Biotechnology, Chemical Engineering, Biological Engineering, Biomedical Engineering, or related field with 4+ years relevant industry experience; or Master’s degree with 1+ year relevant industry experience; or PhD with applicable research and/or industry experience
- DoE and multivariate analysis literacy; ability to design, run, and interpret statistically sound studies
- Working knowledge of scientific computing/data analysis tools (e.g., Python, R, MATLAB, or equivalent)
- Experience producing reproducible analysis workflows (e.g., notebooks/scripts) and translating insights into experimental changes or control strategies
Preferred Qualifications:
- Upstream bioprocess foundation: cell culture and bioreactor operation (fed‑batch, perfusion)
- Scale-up/scale-down fluency and mass transfer fundamentals
- Exposure to mechanistic/kinetic modeling (e.g., Monod‑type kinetics) and practical ML
- Familiarity with data systems and governance (ELN/LIMS, data provenance, version control)
- Clear, first‑principles reasoning; can explain assumptions and design validation experiments
- Experience bridging bench science, process engineering, and data science; strong communication and organization
- 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
- Lead multivariate analyses and modeling (e.g., PCA, time‑series analytics); develop and deploy reproducible analyses (notebooks/scripts) that inform decisions
- Build pragmatic models (mechanistic or hybrid/ML where valuable) and validate with targeted experiments; document assumptions, limits, and outcomes
- Leverage digital tools and data systems to improve process understanding and decision‑making; ensure data provenance and version control
- Translate CFD modeling into actionable scaling and control strategies
- Collaborate across upstream, downstream, formulation, and analytical teams to align priorities and move decisions forward
- Document work in electronic lab notebooks and author high‑quality technical reports and decision memos
- Support tech transfers and regulatory filings with clear, traceable documentation
- Drive innovation by evaluating new bioprocess technologies and modeling approaches; create reusable templates and analysis pipelines to raise team velocity
Basic Qualifications:
- Bachelor’s degree in Biochemistry, Molecular Biology, Biotechnology, Chemical Engineering, Biological Engineering, Biomedical Engineering, or related field with 4+ years relevant industry experience; or Master’s degree with 1+ year relevant industry experience; or PhD with applicable research and/or industry experience
- DoE and multivariate analysis literacy; ability to design, run, and interpret statistically sound studies
- Working knowledge of scientific computing/data analysis tools (e.g., Python, R, MATLAB, or equivalent)
- Experience producing reproducible analysis workflows (e.g., notebooks/scripts) and translating insights into experimental changes or control strategies
Preferred Qualifications:
- Upstream bioprocess foundation: cell culture and bioreactor operation (fed‑batch, perfusion)
- Scale-up/scale-down fluency and mass transfer fundamentals
- Exposure to mechanistic/kinetic modeling (e.g., Monod‑type kinetics) and practical ML
- Familiarity with data systems and governance (ELN/LIMS, data provenance, version control)
- Clear, first‑principles reasoning; can explain assumptions and design validation experiments
- Experience bridging bench science, process engineering, and data science; strong communication and organization