Short Biography

As a data scientist at W&H, Alexander Schnell deals with general topics relating to the digital products that are being developed and in particular with predictive maintenance. The main goal of the latter project is to predict a device defect as accurately as possible based on sensor data so that downtimes and subsequent service costs can be minimized. The spectrum of solution methods is versatile and ranges from machine learning algorithms to methods from time series analysis.

After studying mathematics at the University of Salzburg, Mr. Schnell completed a PhD in Logistics and Operations Management at the University of Vienna. From 2010 – 2015 he also worked there as a research assistant at the Chair of Production and Logistics in teaching and research. In this context Mr. Schnell mainly dealt with exact optimization algorithms for resource limited project planning problems. After completing his PhD studies, he worked for a company that develops software for the logistics industry. His main focus there was on improving the efficiency of route and route planning algorithms in practice. In September 2017, Mr. Schnell accepted the challenge to join the newly formed digitization team at W&H as a Data Scientist.

Panel: Fuck-up Session