Advanced Process Modeling Expert

BioSpace; Cambridge (MA); Remote; USD 90,000 - 120,000; Full time

Job summary

A leading global biopharmaceutical company is seeking an Advanced Process Modeling Expert to develop and maintain complex process models and digital twins. This role involves utilizing AI/ML for optimizing manufacturing processes, applying scientific modeling techniques, and ensuring data-driven decision-making. Candidates should have a STEM degree and at least 3 years of pharmaceutical manufacturing experience, with proficiency in programming and statistical modeling. Strong communication skills and a curiosity for new tools are essential. Remote location considered for qualified individuals.

Qualifications

• Minimum of 3 years in pharmaceutical manufacturing, familiar with cGMP requirements.
• Experience digitizing industrial processes and data analytics.
• Fluent in English.

Responsibilities

• Develop mechanistic, hybrid, and statistical/ML models for manufacturing.
• Build dynamic digital twins for scenario analysis and tech transfer readiness.
• Drive manufacturing optimization initiatives.

Skills

Proficiency in AI

Machine learning

Statistical methods

Programming skills in Python

Scientific modeling

Education

Bachelor’s degree in a STEM field

Master’s or PhD

Tools

gPROMS

Discoverant

SIMCA Online/Offline

AWS

Databricks

Job description

Job Overview

Advanced Process Modeling Expert – BioSpace (Takeda)

Develop, deploy, and maintain advanced process models and digital twins that drive control and optimization strategies across manufacturing processes, from unit‑operations to end‑to‑end. Employ custom code, digital modeling tools, and robust data infrastructure to create mechanistic, hybrid, or AI/ML‑based models for dynamic simulation, forecasting, and soft‑sensing.

Responsibilities

• Develop mechanistic, hybrid, and statistical/ML models for unit operations (e.g., bioreactors, chromatography, filtration, UF/DF) using first principles.
• 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.

Qualifications

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

Location

Zurich, Switzerland & Cambridge, MA.

Equal Employment Opportunity

Takeda is proud of its commitment to creating a diverse workforce and providing equal employment opportunities to all employees and applicants for employment without regard to race, color, religion, sex, sexual orientation, gender identity, gender expression, parental status, national origin, age, disability, citizenship status, genetic information or characteristics, marital status, or any other characteristic protected by law.