Advanced Process Modeling Expert

Takeda

25d

About The Position

As an Advanced Process Modeling Expert, you will develop, deploy, and maintain advanced process models and digital twins that drive control and optimization strategies across manufacturing processes, both for unit-operations and end-to-end. Developing custom code, leveraging digital modeling tools, and robust data infrastructure, you will develop mechanistic, hybrid, or AI/ML-based models for dynamic simulation, forecasting and soft-sensing. Your work will enhance process understanding, support tech transfers, and strengthen overall process capability and product quality.

Requirements

• Bachelor’s degree in a STEM field (Chemical or Biochemical Engineering preferred); Master’s or PhD is an advantage.
• Minimum of 3 years in pharmaceutical manufacturing, familiar with cGMP requirements.
• Experience in digitizing industrial processes, computational modeling, process simulation, soft-sensor development, and data analytics.
• Proficiency in AI, machine learning, and statistical methods for data-driven decision-making.
• Strong expertise in scientific modeling: mechanistic and hybrid modeling; experience with flow-sheeting software such as gPROMS.
• Strong programming skills in Python (Julia is a plus).
• Clear communicator capable of explaining technical concepts to non-experts.
• Curious and open to leveraging new digital tools and methodologies to improve industrial processes.
• Fluent in English

Nice To Haves

• Familiarity with Discoverant, SIMCA Online/Offline, OSI PI, Databricks, and AWS is a plus.
• Familiarity with Agile project management (Scrum/Kanban in Jira) is advantageous.

Responsibilities

• Develop mechanistic, hybrid, and statistical/ML models for unit operations (e.g., bioreactors, chromatography, filtration, UF/DF), applying first principles like mass & energy balances, reaction kinetics, thermodynamics, and transport phenomena.
• Build dynamic digital twins for scenario analysis, scale-up/scale-down evaluation, material-balance verification, and manufacturing tech transfer readiness.
• Own the full lifecycle of digital process models, from conceptual design to implementation, deployment, monitoring, and retraining.
• Prepare and structure data for dry-lab simulations by integrating sensor/historian, batch records, and Manufacturing Sciences lab data.
• Develop soft sensors for non-measurable or slow-to-measure process parameters using mechanistic correlations, ML regression, and hybrid approaches.
• Drive manufacturing optimization initiatives such as cycle-time reduction, yield improvement, process robustness studies, root-cause analyses, and failure-mode simulations.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Number of Employees: 5,001-10,000 employees