Podcast – Episode 25:
Leading and hiring in the data space
Data science and machine learning leader tells all
Data, data, data everywhere! Sometimes I wonder if we’re going to drown in it all. Thankfully we have a super expert on the subject to join us to demystify some of the reoccurring topics in this arena. Roy Keyes, a data science and machine learning guru and expert in knowing what people to hire for this elusive new field of computing, tells us all in the time we had for the podcast. We got so carried away, it’s a long one. To make it easier, if you want to jump into a particular area that interests you, please use the Table of Contents below.
“As a leader, I think that it’s really important that you help people realize this curiosity and desire to learn, to kind of push them along and kind of help them be fulfilled, in a market where experienced people are the hard ones to find.”
Roy’s book, “Hiring Data Scientists and Machine Learning Engineers: A Practical Guide.” can be found here.
The key takeaways from the podcast are:
- What is the definition of
- Data science
- Data analytics
- The various roles that gravitate around this industry
- Some tips as to how to structure data people within the org
- The challenges of hiring the right people for the job
And much, much more
TLDR: Straight to the point
(Quick Links)
We all live busy lives, and sometimes a long podcast is too much of a time-footprint for our busy schedules. So, to help you get to the bit you’re more interested in, use our table of contents below. Quick links to help you get straight to the point.
2. How to build your team with the right people in data science engineering
3. A clear definition of what a Data Scientist is vs. Data Analyst
4. Why is data science happening now versus 20 or 30 year ago?
5. Big Data: is there too much data that we don’t know what to do with it anymore?
6. The tools to manage all the data and the AI and ML approach
7. All the qualities that tick the boxes for being the best Data Scientist and ML engineer
8. Do you have to be an expert in the field and does that help you hire the right people?
10. The patterns of failure in data science and ML
11. The mysteries of the outcomes of a data science project
12. The quality of data
13. Tips on hiring the best data scientists and getting the best out of them
14. Where do the teams of data scientists belong within an organization?
15. Roy’s key takeaways for tech leaders
16. Roy introducing his book “Hiring Data Scientists and Machine Learning Engineers: A Practical Guide”
17. TC's key takeaways from the podcast