How to Become a Chief Data Scientist | T. Scott Clendaniel

On this episode of The Artists of Data Science, we get a chance to hear from T. Scott Clendaniel, a leader in the data science space with over three decades of experience serving in various roles in business, analytics and artificial intelligence. Currently, he's a chief data scientist of the Strategic Artificial Intelligence Lab, where he's aiming to create cutting edge artificial intelligence that can be made accessible to all. He gives insight into the future of A.I, how to be an effective leader, and how to use storytelling in data science.

Scott shares with us his incredible career journey and the insights he has gathered from it. This episode is packed with advice, wisdom, and tips for every data scientist to take something from. It was a great honor interviewing Scott!

WHAT YOU'LL LEARN

[7:57] What is an A.I. winter?

[10:54] Where the field of data science is headed in the next few years?

[13:58] Tips on being an effective leader

[20:39] The underrated skill of storytelling, and how to cultivate it

[32:43] Tips for people that want to break into data science

QUOTES

[16:01] “If you're the first data scientist in an organization...make sure that you focus on a crawl, walk, run approach.”

[17:50] “Simplicity is ridiculously underrated…people do not support what they don't understand. Instead, they fear what they don't understand.”

[35:03] “Find your why and make sure it's the right why and use that to propel you…”

SHOW NOTES

[00:01:35] Introduction for our guest today

[00:03:33] What drew you to the field and some of the challenges you faced while you're trying to break into and create your own lane in Data science?

[00:05:00] How much more hyped has I become since he first broke into the field?

[00:07:39] A brief history of the AI winters we've experienced and why we're on the verge of the next winter

[00:10:54] Where do you see the field of data science, machine learning, artificial intelligence headed in the next two to five years?

[00:12:27] In this vision of the future. What do you think is going to separate the great Data scientists from just the good ones?

[00:13:42] What's it mean for you to be a good leader in Data science. And how can an individual contributor embody the characteristics of a good leader without necessarily having the title?

[00:15:27] For someone who's, let's say, the first data scientist in the organization and they're kind of responsible for building up to Data Science practice, what are some of the challenges that you would see them facing and how do you think that they could overcome those challenges?

[00:18:48] What would you say the hero's journey looks like for a Data scientist or anyone in a data related role?

[00:19:31] The importance of story-telling in data science

[00:22:27] Does the way you tell a story differ if you're talking to your manager versus maybe talking to a roomful of executives? Do you have any tips for Data scientists?

[00:25:32] So what are some questions we could ask ourselves when we're starting a project that can really help us clarify exactly what the problem is?

[00:27:35] There is a hidden Data science message in the movie Dr. Strange

[00:28:47] How do you think a Data scientists could develop and cultivate a business acumen or a product sense for themselves?

[00:30:56] The multiplicity of algorithims and the importance of feature engineering

[00:32:25] Can you share some tips or words of encouragement for our listeners who's got like a couple of decades, maybe 10 to 20 years of non Data related experience under their belt and they're now trying to break into Data science? What challenges do you foresee them facing and how can they overcome these challenges?

[00:35:20] What advice or insight can you share with people who are breaking into the field and they look at these job postings and some of them want the abilities of an entire team wrapped up into one person?

[00:39:28] Do you have any suggestions for finance or fintech Data science projects that an aspiring Data scientists could tackle?

[00:42:15] What are some things that we need to be cognizant of and monitor and track once the model is deployed, both from the Data scientists perspective and the business perspective?

[00:44:22] What advice do you have for Data scientists who have who feel like they don't need to learn anymore? What would you have to say today, scientists in that mindset?

[00:46:51] What's the one thing you want people to learn from your story?

[00:48:58] So what are the two five letter words that really grind your gears and why?

[00:49:06] So what is an academic topic or just an area of research or interest outside of Data science that you think every Data scientist should spend more time researching on?

[00:49:27] What is your favorite question to ask during an interview?

[00:51:00] What's the number one book you'd recommend our audience read and your most impactful take away from it?

[00:52:08] If we could somehow get a magic telephone that allowed us to contact 20 year old T. Scott, what would you tell him?

[00:52:42] What is the best advice you have ever received?

[00:53:09] What motivates you?

[00:53:35] What song do you have on Repeat right now?

[00:53:44] How could people connect with you? Where can they find you?