Konrad is a data science manager with experience stretching longer than he likes to ponder on. He holds a PhD in statistics from Vrije Universiteit Amsterdam, where he focused on problems of extreme dependency modeling in credit risk. He slowly moved from classic statistics towards machine learning and into the business applications world. Konrad worked in a variety of financial institutions on an array of data problems and visited all the stages of a data product cycle: from translating: business requirements (“what do they really need”), through data acquisition (“spreadsheets and flat files? Really?”), wrangling, modeling and testing (the actually fun part), all the way to presenting the results to people allergic to mathematical terminology (which is the majority of business). He has visited different ends of the frequency spectrum in finance (from high frequency trading to credit risk, and everything in between), predicted potato prices, analysed anomalies in industrial equipment and optimised recommendations. He currently leads a central data science team at Adevinta. As a person who himself stood on the shoulders of giants, Konrad believes in sharing the knowledge with others: it is very important to know how to approach practical problems with data science methods, but also how not to do it. Konrad has a bit of a competitive streak, so in his spare time he is active on Kaggle and trains Krav Maga (see the profile picture).
