CARTE industry seminar series welcomes Jeff Schmidt, Data Scientist – Advanced Research and Development Services, The Raymond Corporation.

The seminar is moderated by Ningyuan Chen, Assistant Professor, Department of Management, University of Toronto Mississauga, cross-appointed at the Rotman School of Management, University of Toronto.

Abstract: Social media and high tech companies have attracted the majority of the data science talent pushing innovation and research by leveraging their data rich environments. This has created some interesting challenges for other industries from securing talent to filling large knowledge gaps due to the speed of innovation. Through my experience at a forklift manufacturer I hope to provide a window into off-trend data science. Some of the topics of discussion are the challenges a data scientist will likely encounter in this and related industries and the skills that one will likely need to acquire to be successful, a case study of a data driven lease prediction using XGBoost and forklift service data to more accurately capture ownership costs associated with Raymond forklifts, and a discussion of opportunities in anomaly detection/performance monitoring using high resolution time-series data and component level failure prediction. The manufacturing industry is hungry for talented data scientists, though the role is a bit different from its high-tech counterparts. Hopefully the talk will help prepare future data scientists and provide insight into research areas that could drive innovation in manufacturing and similar industries.

Speaker Bio: Jeff Schmidt is a Data Scientist for the Advanced Research Department for The Raymond Corporation since 2015. Jeff received his BS in Computer Science in 2006, his MS in Systems Science in 2012, and is currently a PhD candidate in Systems Science all at Binghamton University. At Raymond Jeff has worked on real-time data pipelines, productionized ML models, cloud deployment architecture, job-shop scheduling optimization, and other research related projects. Research interests include biologically plausible neural networks, sensory substitution, and an unshakable interest in cryptocurrency.