This week on Papa PhD, I’m talking with Caolan Kovach-Orr about his journey from the wet lab to a career in data science. During our conversation, we talked Caolan’s academic journey, about what skills you should focus on as a candidate, today, if you’re interested in data science, about the specifics of the interviewing process for data scientist positions, and Caolan shared specific advice based on his experience as a PhD in the corporate space, so be ready to take notes!

What you’ll learn about in this episode:

  • Why you shouldn’t compare yourself to others as an atypical candidate
  • What companies are looking for in a candidate, when hiring a PhD
  • Why you should invest in learning Python
  • How a public track record of writing code can help you stand out in the hiring process
  • The importance of letting go of ego when transitioning to a new domain
  • When to start networking with potential employers
  • What an 11-stage interview process look like
  • Salary negotiation best practices

Do you enjoy Papa PhD? Leave me a comment here – one short sentence is enough! And be sure to include your Twitter handle – that way, I can thank you personally!

Caolan K Orr

Caolan Kovach-Orr Caolan earned his PhD in theoretical biology from McGill in 2015, his thesis focused on predator-prey interactions. He started, in industry, as a Data Scientist, and currently heads a data science & engineering team for Verisk Analytics. Caolan and his team continue to work on solving novel problems, such as getting regulatory approval for the US’s first machine learning based insurance pricing product.

Thank you, Caolan

If you enjoyed this interview with Caolan, let her know by clicking the link below and leaving him a message on Twitter:

Click here to thank Caolan Kovach-Orr on LinkedIn!

Click here to share your key take-away from this interview with David!

Caolan’s pearls of wisdom:

“Data science problems now are actually not that hard to code. There have been a lot of people who’ve done a lot of work on open-source packages and, you know, 10 years ago somebody who could create a neural net model probably had to write the code for the neural net. Now there’s hundreds, if not thousands of packages out there. Everything’s kind of pre-configured, so the actual running of a model isn’t that difficult. It may be knowing which model to use, knowing how to design your experiments, how to design your tests, how to design your data that can really make the big difference between something that works really well and something that’s okay. And that’s where you want to be as a PhD, as somebody who can do something really well. Because, honestly, I can teach most undergrads from a comp sci background how to run a neural net in an afternoon, maybe a week. So you don’t want to be competing with them – they can just do it okay at that point. You want to be the person who can bring that next level of value.”

“Moving up the corporate ladder is not about who’s the best. It’s just not. It’s about who can help the most people. So you don’t get that manager, directorship or VP position because you’re the smartest person in the room – you get it because you will be able to help the people who report in to you and the people who you report to the most in that position, and so there’s almost no room for ego.”

This episode’s resources:

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