Senior Scientist, Hybrid Modeler, Digital Insights, Dscs Digital Technologies

Merck Merck · Pharma · NJ

Senior Scientist role focused on developing and deploying mechanistic, CFD-based, and data-driven models for sterile drug substance and drug product manufacturing processes in the pharmaceutical industry. The role involves integrating physics-based models with AI/ML methods to optimize processes, de-risk scale-up, and support clinical-to-commercial transitions. Requires expertise in CFD, programming (Python, MATLAB), and data science, with a focus on applying these to pharmaceutical manufacturing challenges.

What you'd actually do

  1. Develop and deploy a portfolio of mechanistic, CFD, and data-driven models to support development, scale-up, tech transfer, and manufacturing of sterile DS and DP processes across a biologics and vaccine pipeline.
  2. Lead CFD-based mixing and unit operation modeling (e.g., compounding, dilution, pumping, filling, filtration) to quantify hydrodynamic stresses, energy dissipation rates, mixing times, and scale-up risk—enabling science-based operating windows and control strategies.
  3. Integrate data science and machine learning with physics-based models to accelerate model execution, improve predictive accuracy, and enable rapid scenario screening.
  4. Collaborate closely with stakeholders to de-risk sterile process scale-up, optimize formulation and process robustness, and support clinical‑to‑commercial transitions.
  5. Design, execute, and interpret scale‑down and validation experiments to establish model credibility and scalability. Use experimental data to validate and refine CFD and ML models.

Skills

Required

  • CFD and transport phenomena
  • multiphase and complex flows
  • Python
  • MATLAB
  • R
  • JMP
  • data wrangling
  • visualization
  • model coupling
  • workflow automation
  • model validation against experimental data
  • designing representative scale-down systems
  • translating complex modeling results into clear, actionable insights

Nice to have

  • sterile CMC development workflows
  • mixing
  • pooling
  • pumping
  • filling
  • filtration
  • freeze-drying

What the JD emphasized

  • Strong expertise in CFD and transport phenomena
  • Strong programming and data science skills in Python, MATLAB, R, JMP, or equivalent
  • Experience validating models against experimental data and designing representative scale‑down systems.

Other signals

  • integrating CFD with experimental data and AI/ML methods
  • deploying mechanistic, CFD-based, and data-driven modeling approaches
  • building and applying mixing and unit-operation virtual twins