Principal Scientist, Process Modeling, Digital Insights
Merck
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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
Education
Tools
Job description
We are seeking a Principal Scientist to join our Digital Insights team within the Development Sciences and Clinical Supply Digital Technologies organization (DDT). Digital is the multiplier that will allow DSCS to deliver better experiments faster, efficient filing and launch, more robust supply chains and higher-confidence decisions across the portfolio.
We are seeking a Principal Scientist to join our Digital Insights team within the Development Sciences and Clinical Supply Digital Technologies organization (DDT). Digital is the multiplier that will allow DSCS to deliver better experiments faster, efficient filing and launch, more robust supply chains and higher-confidence decisions across the portfolio. The DSCS Digital Technologies organization is responsible for the invention and application of new digital tools/workflows to support scientists across drug substance development, drug product development and analytical development. We aspire to embed digital technologies into the fabric of DSCS culture to drive transformational impact across the CMC space. In this Principal Scientist role, the successful candidate will apply first-principles engineering, computational fluid dynamics (CFD), and physics-based simulation to bring predictive rigor to scale-up across a multi-modality pipeline from small to large molecules, with a current focus on biologics. This role will serve as our senior technical authority on CFD and engineering simulation for drug substance development, with an emphasis on bioreactor scale-up/scale-down and other sensitive bioprocess unit operations. They will lead physics-based assessments of mixing (P/V, shear, pH gradients) and gassing (kLa, O2 distribution) to define engineering equivalence between scales and to inform scale-down model design. The successful candidate will play a technical leadership role in establishing engineering simulation as a core capability across DSCS partnering deeply with experimentalists, process engineers, and DS technical leads to translate first-principles model outputs into actionable CMC decisions. As a senior member of the Process Modeling & Analytics team, they will also mentor junior scientists, shape the group’s modeling roadmap, and champion the disciplined use of physics-based simulation across the pipeline.
- Lead CFD and engineering simulation of bioreactor mixing and gassing to characterize P/V, shear, mixing time, pH and component gradients, kLa, and gas gradients toward defining engineering equivalence between development and manufacturing scales.
- Design scale-down models that reproduce the critical hydrodynamic and mass-transfer environment of the manufacturing-scale bioreactor, in partnership with process development scientists.
- Extend physics-based modeling to other sensitive bioprocess unit operations, including harvest (centrifugation, depth filtration) and TFF (UF/DF, viral filtration), to quantify shear and hydrodynamic risk to product quality.
- Own end-to-end modeling project execution: problem framing, geometry and mesh strategy, solver setup, validation against experimental data, and clear communication of predictions and their limitations to cross-functional stakeholders.
- Support vessel and site characterization studies and standard workplans to support enterprise scale-up/scale-down strategy.
- Mentor junior scientists on the Process Modeling & Analytics team; grow their technical judgment in transport phenomena, CFD methodology, and simulation-based decision making.
- Shape the team’s modeling roadmap and establish practical standards for simulation workflows (case setup, HPC/cloud use, validation, reuse) that scale across the portfolio.
- Support engineering simulations extending from biologics to small-molecule organic drug substance unit operations (e.g., crystallization, reactor mixing) as pipeline needs require.
- Ph.D. in Chemical Engineering, Mechanical Engineering, Bioengineering, Physics, Biology, Chemistry, Data Science or a closely-related field with at least 6 years of industrial/pharmaceutical or relevant experience.
- M.S. in Chemical Engineering, Mechanical Engineering, Bioengineering, Physics, Biology, Chemistry, Data Science or a closely-related field with at least 8 years of industrial/pharmaceutical or relevant experience.
- B.S. in Chemical Engineering, Mechanical Engineering, Bioengineering, Physics, Biology, Chemistry, Data Science or a closely-related field with at least 10 years of industrial/pharmaceutical or relevant experience.
- Chemical engineering training (or closely related) with deep grounding in transport phenomena, fluid mechanics, mixing theory, and gas–liquid mass transfer.
- Extensive hands-on CFD expertise using tools such as M-Star CFD, ANSYS Fluent, STAR-CCM+, COMSOL, or OpenFOAM—including geometry, meshing, solver setup, and post-processing for stirred-tank and gas–liquid systems.
- Demonstrated experience characterizing bioreactors at development and manufacturing scale: quantifying power input, tip speed, shear, mixing times, kLa, and gas hold-up, and connecting those outputs to scale-up decisions.
- Track record of designing scale-down models and defining engineering-equivalence criteria that hold up when a process moves between scales.
- Ability to validate simulations against experimental data and to articulate model credibility, sensitivities, and uncertainty to advise action and decision.
- Scientific leadership and mentorship experience; comfortable growing modeling capability in others rather than only doing the work personally.
- Strong scripting skills in Python (or MATLAB) for pre-/post-processing and workflow automation around commercial CFD tools.
- Direct experience in an industrial biologics setting (cell culture bioreactor scale-up, harvest, or TFF) as a process development scientist or process engineer
- Familiarity with the shear-sensitivity of biologics (cells, proteins) and how interfacial and hydrodynamic phenomena connect to product quality.
- Experience with single-use bioreactors and single-use downstream trains at industrial scale.
- Fluency with population balance modeling and/or multiphase methods (Eulerian–Eulerian, Eulerian–Lagrangian, VOF) as applied to bubbles, droplets, or particulates.
- Familiarity with small-molecule organic drug substance unit operations (e.g., crystallization, reactive systems, liquid-liquid mixing).
- Experience coupling CFD outputs to data-driven or reduced-order surrogates.
- Prior use of modeling and simulation in technology transfer, process characterization, or troubleshooting at scale.
Adaptability, Adaptability, Cell Culture Process Development, Cell Line Development, Chemical Engineering, Computational Fluid Dynamics (CFD), Computer Simulations, Data Analysis, Engineering Leadership, Engineering Physics, High Performance Computing (HPC), Innovative Thinking, Leadership Mentoring, Materials Science, Model Development, Model Driven Design, Multiscale Modeling, Pharmaceutical Process Development, Process Engineering, Process Modeling, Process Simulation, Professional Collaboration, Professional Networking, Quality by Design, Simulation Modeling {+ 3 more}
- Experience with computational fluid dynamics models to predict and optimize bioprocess behavior.
- Expertise in scaling up bioprocesses and evaluating scale-down models.
- Strong analytical and problem-solving skills to interpret CFD results.
- Experience integrating CFD simulations with experimental design and validation.
- Background in bioprocess engineering and understanding of downstream unit operations.
- Skills in programming and data visualization to support process modeling.
Our company is committed to inclusion, ensuring that candidates can engage in a hiring process that exhibits their true capabilities.
As an Equal Employment Opportunity Employer, we provide equal opportunities to all employees and applicants for employment and prohibit discrimination on the basis of race, color, age, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability status, or other applicable legally protected characteristics.
As a federal contractor, we comply with all affirmative action requirements for protected veterans and individuals with disabilities.
For more information about personal rights under the U.S. Equal Opportunity Employment laws, visit: EEOC Know Your Rights
EEOC GINA Supplement
We are proud to be a company that embraces the value of bringing together, talented, and committed people with diverse experiences, perspectives, skills and backgrounds. The fastest way to breakthrough innovation is when people with diverse ideas, broad experiences, backgrounds, and skills come together in an inclusive environment. We encourage our colleagues to respectfully challenge one another's thinking and approach problems collectively.
Learn more about your rights, including under California, Colorado and other US State Acts
The salary range for this role is $173,200.00 - $272,600.00. This is the lowest to highest salary we in good faith believe we would pay for this role at the time of this posting. An employee’s position within the salary range will be based on several factors including, but not limited to relevant education, qualifications, certifications, experience, skills, geographic location, government requirements, and business or organizational needs.
The successful candidate will be eligible for annual bonus and long-term incentive, if applicable.
We offer a comprehensive package of benefits. Available benefits include medical, dental, vision healthcare and other insurance benefits (for employee and family), retirement benefits, including 401(k), paid holidays, vacation, and compassionate and sick days. More information about benefits is available at https://jobs.merck.com/us/en/compensation-and-benefits.
The application deadline for this position is stated on this posting.
We will consider qualified applicants with arrest and conviction records for employment in compliance with the San Francisco Fair Chance Ordinance
We will consider for employment all qualified applicants, including those with criminal histories, in a manner consistent with the requirements of applicable state and local laws, including the City of Los Angeles' Fair Chance Initiative for Hiring Ordinance
Merck & Co., Inc., Rahway, NJ, USA, also known as Merck Sharp & Dohme LLC, Rahway, NJ, USA, does not accept unsolicited assistance from search firms for employment opportunities. All CVs / resumes submitted by search firms to any employee at our company without a valid written search agreement in place for this position will be deemed the sole property of our company. No fee will be paid in the event a candidate is hired by our company as a result of an agency referral where no pre-existing agreement is in place. Where agency agreements are in place, introductions are position specific. Please, no phone calls or emails.
Regular
Domestic
No
10%
Hybrid
Not Indicated
No
n/a
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