Data Science has undergone a rapid evolution over the past 10 years. Whereas Data Scientists were unicorn rock stars 10 years ago, the field has matured and today’s Data Scientists are much more specialized than the generalists of the early days. Today, Data Scientists need to be much better software developers, have a thorough understanding of business and industry domains, and usually specialize in a particular facet of ML (e.g., NLP, computer vision, sensor data analysis, etc.). Data Scientists are measured on tangible value, as opposed to the play and experimentation of the early days. Stefan and Alex have been in this field for more than 15 years and will share some insights on what makes a good Data Scientist today – while also sharing insights from BMW as one of the largest employers of #data and #AI experts in Europe.
Stefan Meinzer stands for digital transformation in the automotive industry and for generating maximum added value from data. He studied business administration at LMU Munich, specializing in statistics and business analytics. While working, he completed his doctorate at the Machine Learning Lab of the University of Erlangen-Nuremberg. He started his professional career at the BMW Group in the area of vehicle data analysis in aftersales before moving to the Volkswagen Group. There, he was responsible for aftersales and logistics in the Volkswagen Data:Lab and completed numerous Data Science projects in a wide range of brands within the Group. In 2017, Stefan Meinzer returned to the BMW Group to work in Sales IT. There, he is now in charge of Cooperate Performance Management, Pricing and Advanced Analytics Sales. The central elements of data governance and the cloud platforms of the global sales regions are also bundled in his domain. With his passion for data and his broad professional and technical interests, Stefan Meinzer tirelessly drives the use of Data Science in various business areas of the automotive industry. In doing so, he pursues, among other things, the strategic goals of improving sustainability, constant customer centricity, and the transformation of existing processes into data-driven workflows.