Principal Scientist Manufacturing Statistics

Johnson & Johnson Johnson & Johnson · Pharma · Beerse, Antwerp, Belgium +2

The Principal Statistician will lead advanced statistical modeling efforts in support of manufacturing programs, focusing on predictive models, spectral deconvolution, chemometric workflows, reaction fingerprinting, and spectroscopic screening. This role involves partnering with chemists, formulation scientists, and process engineers to ensure the quality, safety, and continuous supply of medicines.

What you'd actually do

  1. Lead statistical strategy and execution for projects including solubility modeling and prediction; accelerated stability and shelf-life estimation; reaction trend fingerprinting and outlier detection; dissolution modeling; mechanistic modeling; and spectral analytics (ssNMR, THz-Raman, XRD).
  2. Develop design of experiments and Bayesian Optimization workflows to accelerate method development and reaction optimization.
  3. Lead chemometric and spectral analysis pipelines
  4. Work closely with researchers on experimental design, data analysis, interpretation, and clear communication of evidence to support research, development, and product commercialization.
  5. Co-author scientific publications and present research findings at internal and external forums.

Skills

Required

  • Advanced degree in Statistics, Biostatistics, Chemometrics or a closely related discipline.
  • Minimum 5 years of applied statistical modeling experience in pharmaceutical, biotech, or chemical process environments, or equivalent industry experience.
  • Strong expertise in Bayesian and frequentist methods, including experience with design of experiments.
  • Hands-on programming proficiency in R and/or Python.
  • Demonstrated ability to lead cross-functional collaborations and to present complex statistical concepts clearly to non-statistical audiences.
  • Advocate of reproducible code, robust documentation, and sound data governance practices.
  • Strong communicator capable of building trust across scientific, engineering and product teams.

Nice to have

  • Ph.D. preferred

What the JD emphasized

  • Minimum 5 years of applied statistical modeling experience in pharmaceutical, biotech, or chemical process environments, or equivalent industry experience.
  • Strong expertise in Bayesian and frequentist methods, including experience with design of experiments.
  • Hands-on programming proficiency in R and/or Python.