Carlos Alberto Ortega Zuñiga
Pat Scientist Iii, Manufacturing Sciences @Thermo Fisher Scientific
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WORK HISTORY
Pat Scientist Iii, Manufacturing Sciences @Thermo Fisher Scientific
Гринвилл, NC, US
EDUCATION
University of Puerto Rico-Mayaguez
Doctor of Philosophy (Ph.D.), Applied Chemistry
University of Puerto Rico-Mayaguez
Master’s Degree, Applied Chemistry
University of Chicago
Python for Data Science, Programming language
Universidad de Cartagena - Colombia
Bachelor’s Degree, Chemistry
SKILLS
ABOUT CARLOS ALBERTO ORTEGA ZUÑIGA
I work at the intersection of spectroscopy, chemometrics, and pharmaceutical manufacturing, focusing on how measurement systems and data-driven models are used to understand real process behavior. My work centers on translating analytical science into practical manufacturing applications, especially in environments where process dynamics, sampling limitations, and model assumptions directly affect decision-making.My background spans applied chemistry, computational chemistry, spectroscopy, and multivariate analysis. During my academic training, I worked on molecular simulations, standoff detection methods, and, later on, near-infrared calibration models for continuous pharmaceutical manufacturing in collaboration with Janssen Ortho LLC.After completing my Ph.D, I joined Rutgers University\'s Center for Structured Organic Particulate Systems, where I progressed to Assistant Research Professor. There, I worked on pharmaceutical engineering, process understanding, spectroscopy-based monitoring, and data-driven modeling, while also publishing research, developing proposals, and mentoring students.Since September 2025, I have worked as a PAT Scientist in Continuous Manufacturing at Thermo Fisher Scientific in Greenville, North Carolina. In this role, I continue applying spectroscopy, chemometrics, and advanced analytics to pharmaceutical manufacturing, with emphasis on process understanding, model development, and the practical realities of implementation.Across both academia and industry, I have been particularly interested in the gap between technical claims and manufacturing reality: how we define representativeness, how much confidence a model truly deserves, and how analytical tools can be used in ways that are scientifically sound and operationally meaningful.
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