Kashif is a technology expert and Artificial Intelligence Evangelist. As a serial open source collaborator and contributor, he's not only applied cutting edge technologies to problems as diverse as geospatial web applications, demand forecasting and predictive modelling but has helped build to the likes of PyTorch, Tensorflow and other exciting open source frameworks. Therefore he sees through the hype and can find the tools that are a good fit for your organization.
During Kashif's career as an Artificial Intelligence researcher and practitioner, he has given back to the community by presenting his work at countless conferences, meetups and events. In doing so, he's become a master communicator who ditches complicated jargon and instead brings concepts to life in an understandable and accessable way.
Delivering results for her clients and teaching her students how it's done is Katharina's passion. As a freelancer, she's executed data projects in fields ranging from demand forecasting in food retail to building a data governance pipeline within a fortune 500 company, allowing them to apply AI and unlock their data's value. Her key to success is first understanding all stakeholders and what it is they really need, uniting everyone around a single vision and relentlessly pursuing that unifying goal.
As a lecturer for the "Hochschule für Technik und Wirtschaft Berlin," she's taught countless students the skills needed to manage successful data science projects. Her depth of knowledge and teaching experience make her a top resourse for leaders expanding their data and AI horizons.
Growth is the name of the game, and since completing his Mathematics degree Calvin has been an active part of Zalando's data science success story from infancy to the machine learning juggernaut it is today. Along the way, spearheaded projects bringing data governance and artificial intelligence principles to fields as diverse as warehouse logistics, size recommendations, fraud prevention and AI assisted fashion generation.
In all these areas, he's realized that political obstacles are often more challenging than technical problems, especially when trying to establish a data focused culture in departments new to data science. Therefore, he always takes time to build trust and discuss not just the technical, but also the human factors that drive successful data teams and projects.