Daniel Wintz is a highly accomplished data scientist with a strong background in applied physics. He earned his Master of Science degree and his Doctor of Philosophy from Harvard University and his Bachelor of Science from the University of California, Berkeley, all in Applied Physics. Daniel has worked for several organizations, including Lineage Logistics, where he currently serves as the Principal Data Scientist. His extensive expertise in data science has enabled him to help companies understand complex data sets, identify trends, and make informed decisions. Besides that, he has collaborated with numerous research teams and has several research publications to his credit.

Daniel has gained valuable extensive experience working in various positions in the field of applied physics, including research, data science, consultancy, and instruction. In his current role, Daniel has been responsible for developing and deploying machine learning models that leverage warehouse data to drive improvements in operational efficiency, utilizing data to improve product quality and build a robust end-to-end data platform. He has also worked as a temporary member of technical staff at The Aerospace Corporation and as a research associate at Lawrence Berkeley National Laboratory.