27 Oct 2022

Marcos Huerta | Translating academia experience to data science

Manager of Data Science at Carmax
We will be joined by Marcos Huerta, Manager of Data Science at Carmax. After a PhD. in Astrophysics and a decade in DC science policy, Marcos changed careers to data science in 2019 and now works on pricing algorithms and systems at Carmax.
portrait of Marcos Huerta in front of dark blue textured background

Episode notes

We were joined at the Data Science Hangout by Marcos Huerta, Manager of Data Science at CarMax.

 

A few snippets from the conversation with Marcos at 36:40:

 

What was the most effective asset that you had that led to your transition to your current position?

 

  • A willingness to just try and teach myself new things. In graduate school, I’d done a ton of data analysis, but it was all in an obsolete language that wasn’t going to help me. I had to teach myself Python and R. I think the openness to trying that was key.
  • I gave myself a project to figure out how to use Python and understand classes and object-oriented programming, which I did not understand 10 years ago.
  • I do think that my work experience — because I had done a lot of non-technical stuff — helped as well. I had this record of professional accomplishment that maybe wasn’t technical but people knew I could think and I had this track record. 

 

How do you do the mental folding to translate the many years you spent getting a PhD in astrophysics to a different position?

 

  • The first step for me was not to data science, it was to the science policy world. I think I always had this interest in politics and the government. That first transition came because of these talks at Rice that happened once a month about non-academic careers. Someone who had done science policy and worked at the National Academies of Science and as a Congressional Science Fellow came and gave a talk about what she had done with her physics or astrophysics degree. That really fascinated me.
  • From science policy, data science was more practical. There’s a ton of jobs at the entry level for science policy but as you start to work your way up I was running out of things to do. There become fewer and fewer job openings.
  • Turns out when I started itching that part of my brain again, I really enjoyed it. I enjoyed the Data Incubator. I enjoyed trying to do the swirl lessons in R, building some Shiny apps, etc. Once I got back into doing technical stuff, I found it was still very satisfying.

 

*non-verbatim transcription, summary of a few insights




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