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Tier-1 employer and metro location increase applicants, but PhD and niche chemometrics expertise reduce competition.
Requires deep chemistry/chemometrics domain knowledge combined with ML research skills, limiting cross-industry transferability.
PhD requirement, publications, and specialized chemometrics plus advanced ML skills create stringent hiring filters.
Develop and implement advanced chemometric, statistical, and machine learning models for chemical and process data, enabling process understanding, monitoring, and optimization.
Collaborate with chemists and engineers to translate complex physicochemical behavior into computational solutions and scalable digital workflows.
Ensure model robustness, credibility, and real-time applicability through rigorous validation and incorporation of physics- and chemistry-informed AI approaches.
PhD in Applied Statistics, Data Science, Chemometrics, Mathematics, or Engineering with experience in large-scale parameterization and process data modeling.
Proven expertise in chemometric and multivariate statistical methods applied to chromatography and spectroscopy data.
Experience in deterministic and stochastic optimization and advanced AI methods (e.g., reinforcement learning, GANs) for industrial process modeling.
Work Experience Required: Significant experience delivering strategic R&D and consultancy projects; specific years not explicitly mentioned in the JD.
Strong interdisciplinary expertise at the interface of mathematics, statistics, engineering, chemistry, and digital technologies applied to industrial chemical and energy systems.
Ability to operate independently and collaboratively in multidisciplinary environments from research through deployment.
Track record of scientific leadership including publications in chemometrics, applied statistics, and AI methods targeting process data modeling and optimization.