In this episode of In-Ear Insights, listen as co-founders Katie Robbert and Christopher Penn discuss the skills of a modern data scientist. What should a modern data scientist be able to do?

Who is cut out to be a data scientist?

What are the foundational skills people should learn who want to pursue data science?

Tune in now!

Machine-Generated Transcript

What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Christopher Penn
Alright, so today we’re talking about the skills of a data scientist, and what data scientists do or don’t do. And I think one of the things that, you know, to start off is, when we look at data science, we look at sort of the three P’s, right? The people, process and platform, and you need people who have aptitude, you need processes in place, and you need the the, the system’s themselves. And so I guess I would start by asking you, when we’re talking about data science and things, and we’re talking about people who are performing that role, one of the attributes that is really to me is so critical is that is curiosity. They have to be curious people, when, when confronted with a situation, they have to be willing to ask questions of it, not immediately, just kind of give up, how do you as a manager, as a, as a business leader, encourage and develop people to, to how you teach them? curiosity,

Katie Robbert
one of the main things that you can do as a mentor or a coach or manager to encourage curiosity is to just not give them the answer. Now, it’s not nearly as simple as that, because you don’t want to halt all work production just to make a point, and you don’t want to frustrate someone to the point of them, just quitting, but really posing the questions back. So if they’re coming to you with something that, you know, is a very easy answer, you know,

pose it back to them. Well, what do you think? What would you do in this situation, and really get them to start to think about how they would solve those problems, or additional questions that need to be asked, you can’t outright teach curiosity from a textbook, there’s no formula to say, if you do X, Y thing, then see will be the response equals curiosity. It’s really one of those intense skills, you know, I know that you don’t like the term soft skills, because it’s still an important skill. It’s one of those things you have to keep working at over time. And, you know, to your point,

Christopher Penn
go ahead. But last, what if somebody like for example, you have someone like, like Steph Curry, he’s got incredible eye hand coordination. Now, he practices the living daylights out of you know, six, 810, 12 hours a day practicing shooting a basketball from pretty much anywhere, you could stand in the top of the stands and, and nail every pointer.

And he’s not, I think he’s like, two inches taller than I am. He’s not a super top or somebody has has that aptitude. If he stops, and I’ve practiced for 30 years, I still would not be able to do what he can do today. You know, assuming a magical time machine, you know, going back in time, it’s just not there. So, how do you can you can you develop that somehow in somebody? Or is it one of those things where, like, either, yes, the person has innate curiosity, hire them, and, or if they don’t have innate curiosity, they’re not a good fit the data science role, it’s more

Katie Robbert
complex than that. So in, in that example of, you know, being able to shoot a basketball, he has a motivation, he has a desire to be better, you know, I can learn how to shoot a basketball, but I have no desire to be a great basketball player. So I’m lacking that component of developing the skill, which is actually wanting to do it. So in the example of curiosity, you can develop curiosity in somebody, but if they inherently don’t want to be a curious person, or don’t want to be asking the question, then it’s a lost cause. So you have to have those two sides where

Christopher Penn
they don’t have to be curious to start, but they have to want to be curious, in their end game you have, I feel like, Well, you know, we, we’ve both, you know, share the management of, of, you know, people in on different teams in the past. And, you know, I can think of one person in recent years, apple, they were curious, they wanted to be the next me, and, you know, the, they want to be the next time they had the motivation, but they, they, they, they, they lacked something that allowed them to take that they had the motivation, they have the curiosity, but they didn’t have something else that would get them there. Well, look,

Katie Robbert
it’s, it’s this, it’s the same example. So you have to have the desire, but then you also have to be willing to put in the work to practice to do it. So, you know, again, sort of going back to your example of Stephen Curry, he has the desire to be good, any practices day in and day out to be good. So if you want to be a data scientist, you have to have that desire to be curious. But then you also have to challenge yourself and practice whatever that looks like on a regular basis. to hone that skill set. You can’t just wake up one morning and say, I’m curious, I’m a data scientist, you have to work at it as well, I think it’s a really good point. Because there’s also a very

Christopher Penn
difficult balance for people in in the data science profession, because you have to be curious and unstructured in your thinking. But you also need to adhere to the the relative rigidity of the scientific method. If you’re not following the scientific method. If you just kind of doing stuff, then you’re not really being scientific. And so that that data science, the science part of that data science also tends to go missing. So given that you have such a complex mash up of attributes, how do you find people like that? I mean, there’s, there are a number of different ways companies have tried to do that. Google most famously asks, like, really impossible brain twisting questions. But now what we’re discovering 10 years, 1520 years in is that a lot of those entrance exams did not produce substantially better candidates for those companies than standard recruiting processes. So how do you how do you find someone who has those data science skills, the hard skills and soft skills when the hard skills are easy? You can ask technical question, but how do you find the soft skills?

Katie Robbert
Well, I think that’s, you know, you’re hitting on it very clearly. So it’s the same thing with hiring a candidate for any position, you, as the hiring manager, have to decide, what do I need right off the bat? And what am I willing to train someone to do? Or what can you easily train somebody to do? And what do they inherently have to be. So in the, in the example of a data scientist, so to your original point, curiosity is one of those really important things, having business acumen having good communication skills, those are things that you probably want somebody to walk in the door with, and then they have to, and then have the aptitude to learn more about statistics to learn more about math, they don’t have to know everything, but you need they need to be able to demonstrate that they are able to learn those specific things. So it’s really deciding what things you need right away what you can live with what you can’t live with.

Christopher Penn
Okay, so Ciao, question, then

if suppose you are the hiring manager, and you’re hiring for a data scientist or machine learning and artificial intelligence person, suppose you are you yourself are not that person? How do you know? How do you even know what to ask about because one of the things that I have seen, you know, sort of problematic is,

people who are hiring for these positions or managing these positions don’t actually know how to do the work. And thus, they are unable to distinguish between quality of candidates. Like if, for example, if a candidate came to you and said, Well, you know, I’m really good at Charisse and TensorFlow, and I can do all sorts of fun things with soft Max’s and drop out layers and things like that, if you don’t have that in your lexicon, and you know, what those things are, how do you know what the person is, is, is giving you a load of what the fashion say excrement, toe to toe.

Katie Robbert
So I’ve actually hired a lot of folks outside of my realm of understanding. So I’ve hired engineers, database architects, etc, etc. So it really comes down to feeling comfortable asking them questions, getting them to explain to you what they’re doing, or really seeing how they use their problem solving your critical thinking. So one of the things that we used to do with a database architect is we would put a piece of paper in front of them, and ask them to draw out give them a scenario of something that we were trying to build or develop and have them draw out just at a high level what that architecture of the database should look like. Now, I don’t know whether it’s right or wrong, what I’m looking for is their ability to ask me more questions about what I’m going after, or to use their thinking to say, well, it could look like this, or it could look like this. And you can, if you have a good bs meter, you know, if somebody’s lying, or trying to just sort of fake their way through it. And that’s really what I would be looking for is sort of is those problem solving skills, the critical thinking skills, the communication skills, not whether or not they’re the expert, because I don’t know whether they’re the experts. So in that example, if I’m trying to hire a data scientist, and they say, you know, they start rattling off terms such as TensorFlow and those things I would, I would say, OK, pretend I’m the stakeholder and you’re trying to convince me, how do you explain those things to me, so that I’ll buy into whatever it is that you’re creating, or whatever it is you need, or what you want to do. So there’s ways to ask those questions where you don’t have to be the expert in the thing that they’re the expert in, but you can gauge whether or not they’re the right fit for what you’re looking for. Yeah,

Christopher Penn
makes sense. Makes sense. And I think I would add on to that data science itself is sort of a combination of three things, it is the technical skills, but going back to what your original questions, you know, the difference between a data analyst and a data scientist is not the technical skills, because, because that’s relatively straightforward stuff. Because on the one hand, you have technical skills, on the second hand, you have a deep understanding of statistics of mathematics of probability, because that’s what a good chunk of data sciences and a very large chunk of machine learning and AI is. And then the third branch is really what you’re talking about here, which is the ability to communicate with the business user with the to say, if, if somebody, hey, you probably don’t even care what’s inside this box of stuff, what you do care about is what it’s going to do for you. And it’s the marriage of those three things that determines like, this is a person who is a good data scientist versus this is somebody who took a 299 course on new Dasa T. And, you know, and has a data center certificate, but actually can’t do anything.

Unknown
So yet another unrealistic expectation, a data scientist has to have three hands

Christopher Penn
metaphorically, if you find somebody who actually has three hands. Cool.

Katie Robbert
No, and I think that that’s exactly it. So a data scientist is as a rather complex role, where you’re using the left side of your brain in the right side of your brain a lot of time simultaneously. So you need to be able to do you know, that deep programming and learning a statistics, and then also be able to communicate it in such a way that other people understand it. And that is something that I’ve experienced, where you that’s a unicorn of a person, someone who can do the technology, but also explain it, that’s a really hard thing to find. Yeah,

Christopher Penn
it’s, it’s a difficult thing to find. And as we have found in our past, it is almost impossible to train, you can, like you said, you can develop some of the technical skills things. But if the person doesn’t have the aptitudes, the it’s it’s a long, difficult just log, so what

Katie Robbert
do you do with that? How does someone even start to, to understand whether or not they’re cut out to be a data scientist?

Unknown
Yeah, I have no idea.

Unknown
I mean, let’s take a step back, what led you down the path, say, you know, what, data science, I have the aptitude for this

Christopher Penn
data science, I don’t have the aptitude for it, I’m in the sense of, you know, I am not, I am not particularly good at following process, I can write code and the code is the is my proxy for process because the code is, you know, the code repeats the process so that I don’t have to remember those steps. But I’m actually a relatively poor data scientist, particularly when you compare me to, you know, people who have, like, five PhDs and stuff, I have none. But what led me down the path of trying to do and balance all these different things is that it was frankly, business needs from the first companies I worked out all the way to, you know, co founding brain trust insights, we see a business needs for someone to be able to do good enough work in all these areas, to get answers that create meaningful business impact.

Unknown
That’s,

Christopher Penn
that’s really where I think I ended up was okay, if I can do enough and right enough code to make this thing work, then eventually, we can take a business and grow that business to the point where we can hire somebody who can do a better job of it. But in the beginning is good enough to say, I’m not a web developer, I’m a, you know, I’m, I can, I’m okay at things like WordPress and stuff. And it’s good enough, it’s good enough for now, until you get to a point where you can get better resources to do a better job and to, to scale up. But

I guess it’s like jack of all trades, master of none, kind of kind of situation. And that’s something I look for in in a person is we we talk about one of the most popular things in hiring the last 20 years has been this whole idea of this T shaped person who is a broad generalist a little bit, and then, you know, as a significant specialization, and now Silicon Valley in particular, has gotten to the, the fatty where they’re there, they’re good at everything, which is

Unknown
possible, it’s so wildly unrealistic,

Unknown
but I think it’s

Christopher Penn
the, the core attribute that I have that makes, it still makes me still a functional employee is that curiosity, that willingness to say, I’m going to go and try this out, and it won’t be the best, I will guarantee you, it will not be the, you know, the top shelf best thing that money could buy, but it’ll be good enough, it’ll get you to where you want to go. So

Katie Robbert
let me ask you this question. Because I think this is something that people also don’t fully understand about what a data scientist is,

do you have to be profession in machine learning and artificial intelligence? Or can you just have a really deep understanding of statistic and analysis, I’m sorry, say that again. So when we talk data scientists, we often circle back around to deep learning, machine learning artificial intelligence are those essential things to the skill set of a data scientist, it’s the reverse. So though, those things depend on someone’s data science capabilities, because

Christopher Penn
if we think about, go back to the Rumsfeld matrix, the known knowns, the unknown knowns and so on, and so forth, the known knowns that’s where machine learning and AI are really proficient at helping create solutions, because you’ve got the answers and, you know, you got the answers were data science, it, you see a result is he uses in the unknown knowns, we don’t know what we know. So let’s find out, you know, what’s in the box, what do we have, or we know what we don’t know. And we need external information, or augmentation, a feature engineering and those the as we’re data science plays role in helping transform what’s unknown, into what’s known. And then once it’s known, then we can train a machine on it, build processes, build technology, build platform, all that stuff, if we don’t have those, those know, unknowns solved, then

AI and machine learning actually will break down will will actually be in in make ourselves in a worse situation than if we had, you know, didn’t do it at all, because we’ll be training machines on incorrect or wrong data. And so data science is a prerequisite to machine learning. Well,

Unknown
so then let me ask you this, can you be a data scientist and never touch AI machine learning? Absolutely, yes, it just like, you can be a statistician without ever being a data scientist, I,

Katie Robbert
that one’s easier for me to wrap my head around. Because being a statistician feels like it’s an element of being a data scientist versus the other way around. You don’t need to be a data scientist to be a statistician.

Christopher Penn
Um, I would, I would disagree with that you need that statistics background to be a data scientist, if I was interviewing somebody, one for data science position. And I asked them to tell me the difference between a type one or type two error and they could not that person is not a data scientist, they don’t have the fund the foundational skills they, you know, they probably took one of those crash courses. And sure, that means they’re a button pusher. But that doesn’t mean that they are, they are someone who understands because the value of data science and the value of a good data scientist is not the ability to, to do things, right. And to make stuff, you know, just operate from beginning to end, the value is when something goes wrong. And it will in so many ways when something goes wrong. A qualified data scientist with good foundational skills can go I know what’s wrong is this or it’s this, there’s a selection bias. There’s an attribution bias, there’s this, the sample size was wrong, and they can help you fix the underpinnings of what is likely leading to the problems in your experiment, just like a good chemical scientist could be able to say, Oh, well, your beakers contaminated. That’s why your results are off

without those foundational skills and you don’t have data scientists? Well, I

Katie Robbert
guess that’s what I was saying is that if you’re a statistician, that doesn’t mean you’re a data scientist. If you’re a data scientist, it means you’re probably a pretty good steps. Tisha,

Unknown
you’d better be and but but found a challenge. It’s a

Katie Robbert
foundational skill, but it’s not a year this or your that you have to be a statistician in order to be a data scientist

Christopher Penn
to be a credible one. Yes, yes. And they’re in there in lies. The problem that so many companies have right now is that companies are advertising and paying top dollar for data scientists. But, you know, going back to what we’re discussing earlier, there are a whole bunch of people who are claiming the title without the experience to back it up. So

Katie Robbert
to wrap it up, where does someone start to explore courses or reading about how to become a data scientist? What advice would you give somebody who has the aptitude, let’s assume they have the aptitude and they want to become that, where would they start? So

Christopher Penn
exactly where we were just talking about, you’ve got to be a good statistician to be a data scientist. So take a good statistics course if you can take a statistics course pass it and feel comfortable and and you can enjoy it then guess what you have the mathematical underpinnings to be a good data scientist, if you take statistics and you hate it, and it is not your thing. Guess what you can do? You can work with these different technologies, but don’t pursue the formal data science route. I

Katie Robbert
would add to that, make sure you take a few business courses or read a little bit about communication within business because that’s such an important part of being an effective data scientist is being able to explain and articulate what it is that you’re doing.

Christopher Penn
Exactly. So as always, thank you for listening. Please subscribe to the YouTube channel into the newsletter and we will talk to you soon.


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