Lead CFD Scientist, Bioprocess Scale-Up & Digital Insights

Merck

Rahway (NJ)

Hybrid

USD 173,000 - 273,000

Full time

34 hours ago
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Benefits offered by this job

Hybrid work arrangement
Benefits package

Job summary

Merck seeks a Principal Scientist in Rahway, NJ for the Digital Insights team within DSCS-DDT to apply first-principles modeling, CFD, and physics-based simulation across biologics scale-up. You will lead engineering simulations, mentor staff, and drive cross-functional decisions in CMC development.

The role focuses on defining scale-equivalence, validating models against experiments, and shaping the modeling roadmap for DSCS, with hybrid work options and a strong emphasis on leadership and

Qualifications

  • Ph.D. with 6+ years industrial/pharma experience.
  • Hands-on CFD with listed tools and post-processing.
  • Experience scaling bioreactors development to manufacturing.
  • Ability to validate simulations against experimental data.
  • Strong scripting in Python or MATLAB for CFD workflows.

Responsibilities

  • Lead CFD and simulation of bioreactor mixing and gas transfer.
  • Design scale-down models reproducing manufacturing-scale environment.
  • Extend modeling to harvest and TFF to quantify shear risk.
  • Own end-to-end modeling projects from framing to communication.
  • Mentor junior scientists and shape the modeling roadmap.
  • Support enterprise scale-up/scale-down strategy.

Skills

Transport phenomena
Fluid mechanics
Mixing theory
Gas–liquid mass transfer
CFD
Python
MATLAB
Workflow automation

Education

PhD in related field
MS in related field
BS in related field

Tools

M-Star CFD
ANSYS Fluent
STAR-CCM+
COMSOL
OpenFOAM

Job description

Merck seeks a Principal Scientist in Rahway, NJ for the Digital Insights team within DSCS-DDT to apply first-principles modeling, CFD, and physics-based simulation across biologics scale-up. You will lead engineering simulations, mentor staff, and drive cross-functional decisions in CMC development.

The role focuses on defining scale-equivalence, validating models against experiments, and shaping the modeling roadmap for DSCS, with hybrid work options and a strong emphasis on leadership and

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