Best New Hire: Making Great Data/Analytics Hires With Matt Cowell of QuantHub

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Jun 29, 2020
This article is part of a series called Podcasts.

Editor’s Note: ERE is the new place where you will find the first run of the Best Hire Ever podcast, which explores a simple truth: There’s nothing that drives success at your company like making a great hire.

We are starting on episode 6, so if you want to get caught up, head to the show page to listen to episodes 1-5. 

In episode 6 of Best Hire Ever, Kris Dunn chats with Matt Cowell of QuantHub on how to make great data and analytic hires for your organization. Matt and KD dig into the three different levels of hiring for data fluency in your organization and what’s important with each type of hire. KD takes one for the team and asks all the basic questions you’ve been avoiding because you don’t want to look like you don’t get it.

Give this episode a listen and you’ll be able to talk shop with the geeks, ranging from descriptive to prescriptive analytics, DATA LAKES (wtf) and have a sense for the ongoing education needed to keep the data savants you’re lucky enough to hire for your organization happy and, yes, retained.

Show Highlights:

1:50 – How do you pronounce data? Matt shares that Kris had it right! Major W right off the bat for KD.

3:10 – Definition of QuantHub — What does the company do? They figure out if you analytic and data hires know what they are talking about, and they help upskill them once they are in the door.

4:55 – Matt talks to KD about being data fluent in an organization? What does that mean? Matt breaks down descriptive analytics (what just happened) vs predictive/prescriptive analytics. Turns out the lower level is just as important as the higher level.

7:20 – KD asks Matt to define the difference between three analytic/data jobs in the modern organization: Functional Analyst, Data Engineer, AI Specialist.  Let’s talk about those roles a bit.

12:35 – Matt gets cornered and has to describe what a DATA LAKE is, driven by tools that can do self discovery of data sets.

17:20 – Matt talks about relevant examples of AI in the HR and Recruiting worlds, focusing on video interviewing, as well as text and chat recruiting.

19:50 – Matt talks about natural language processing vs simple voice commands and the link to the concept of AI.

21:20 – KD talks about what he’s learned from Matt to deal with buzzwords on intake calls related what’s required in the data job family.

26:30 – Matt talks about the need to use tools like QuantHub to upskill FTEs in your organization, especially since there aren’t enough candidates to go around.

29:00 – KD and Matt talk about traditional training vs the concept of shorter cycle training that is curated from the web.

32:35 – KD challenges Matt as a data geek to give him his favorite analytics data points from basketball and baseball. He points to 3pt shooting and its impact on PPP in hoops, and launch angle and its impact on home runs and KOs in baseball.

This article is part of a series called Podcasts.
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