InfoQ Podcast - Building Data Science Capability

In this podcast, recorded live at QCon.ai, Principal Technical Advisor & QCon Chair Wes Reisz and InfoQ Editor-in-chief Charles Humble chair a panel discussion with Stephanie Yee, data scientist at StitchFix, Matei Zaharia, professor of computer science at Stanford and chief scientist at Data Bricks, Sid Anand, chief data engineer at PayPal, and Soups Ranjan, director of data science at CoinBase.

Why listen to this podcast:

 - Before you start putting a data science team together make sure you have a business goal or question that you want to answer;  If you have a specific question, like increasing lift on a metric, or understanding  customer usage patterns, you know where you can get the data from, and you can then figure out how to organise that data.
 - You need to make sure you have the right culture for the team - and find people who are excited about solving the business problems and be interested in it.  Also look at the environment you are going to provide. 
 - Your first hire shouldn’t be a data scientist (or quant).  You need support to productionise the models - and if you don’t have a colleague to help productionise it then don’t hire the quant first.
 - Given the scarcity of talent it is worth remembering that Data Scientists come from a variety of different backgrounds - Some people have computer science backgrounds, some may be astrophysicists or neuroscientists who approach problems in different ways.
 - There are two common ways to structure a data science team: one is a vertical team that does everything, the other, more common in large companies, is when you have a separate data science team and an infrastructure team.

More on this: Quick scan our curated show notes on InfoQ https://bit.ly/2Jym1RI

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InfoQ Podcast - Building Data Science Capability

Bijesh