Data Science and what to do with it by Michael Leznik

Join us for a Fireside Chat with Michael Leznik, VP of Data Science at Product Madness, hosted by Dave Loughlan, Founder of Data Idols.

For the last fifteen years, Michael has helped to create and managed interdisciplinary and data science-related teams. He spent five years as Chief Research Scientist at one of the UK’s leading agencies, Greenlight, where he built the whole analytics function from ‘ground zero’. He spent almost five years at King, where he helped build a data science org from less than a dozen scientists to nearly one hundred, managed the London studio and later marketing data science teams. In between, he spent some time consulting digital ‘real’ money casino companies. Working in several startups in the digital marketing area helped him to be on the cutting edge of technology and observe the dramatic developments of the market for the last 20 years. As the VP of Data Science at Product Madness and PixelUnited, Michael oversees all scientific data-related analysis.

This session has something for everyone!

Are you a Data Science Manager or an aspiring Data Science Manager? We will be discussing how Michael has structured and managed teams to deliver data products for some of the largest data companies in the world. Are you a Data Scientist working to build a career? We’ll be discussing what it is to be a data scientist, the nine typical role profiles and the key skills needed in each. Do you work in the wider tech or product organisation? Michael will be offering key insights and takeaways about how to work with cross functional teams and ensure you gain the most from the data scientists in your business.

As said by professor Dan Ariely “Data Science is like teenage sex: everyone talks about it, nobody knows how to do it, everyone thinks that everyone else is doing it.” In this discussion, we will try to uncover all the possible mysteries and misunderstandings related to the DS field. We shall touch upon the fact that the term data science is a huge misnomer and try to understand what it means to be a data scientist today. We will cover hiring a data scientist and what potential employers might expect from the new hire. We will also talk about how to organise a data science org and the structure of those organisations that exist today.

Supported by