process modeling scientist/expert 流程建模科学家.

9. Cross-functional Collaboration & Documentation
10. Fluent English speaking and writing capability

EDUCATION, EXPERIENCE, AND SKILLS
Required
1. M.S. in Chemical Engineering or closely related field with relevant industrial experience in crystallization, particle engineering, or process modeling; or B.S. with directly relevant experience.
2. Hands-on experience implementing PAT for crystallization (e.g., FBRM, PVM, Raman, FTIR), including probe selection, chemometric model development, calibration/validation, and data quality assurance.
3. Demonstrated expertise in crystallization kinetics, PBM, and phase equilibrium/thermodynamics, with a track record of translating models into process designs (seeding strategies, cooling/antisolvent profiles, continuous crystallization).
4. Proficiency with modeling tools such as gPROMS, Aspen Plus/Custom Modeler, Dynochem, MATLAB, Python, or equivalent for PBM, parameter estimation, and uncertainty analysis.
5. Practical understanding of mixing, heat/mass transfer, and their influence on nucleation/growth; experience with CFD or mixing analyses applied to crystallization.
6. Ability to design and execute lab and pilot studies to generate high-quality data for model development and validation.
Preferred
1. Familiarity with solid-state chemistry, polymorph/solvate screening, and form selection.
2. Experience with multivariate data analysis and AI/ML for soft sensing, parameter inference, fault detection, and real-time optimization.
3. Strong technical communication skills and a track record of cross-functional collaboration and presentations/publications.
Knowledge and Skills
1. Analytical and Problem-Solving: Hypothesis-driven approach; competency in sensitivity and uncertainty analysis.
2. Teamwork: Effective collaborator across chemistry, engineering, analytical, automation, and manufacturing.
3. Communication: Clear, concise technical writing and presentations for diverse audiences.
4. Organization: Strong prioritization and project management to manage multiple workstreams.
5. Knowledge Sharing: Document models, datasets, PAT methods, and learnings to build organizational capability.
6. Resource Management: Efficient use of internal labs and external partners/vendors.