SDS 483: Setting Yourself Apart in Data Science Interviews

This is episode number 483 with Andrew Jones, creator of Data Science Infinity. 

Jon Krohn: 00:00:12

Welcome to the SuperDataScience Podcast. My name is Jon Krohn, chief data scientist and bestselling author on deep learning. Each week, we bring you inspiring people and ideas to help you build a successful career in data science. Thanks for being here today, and now let’s make the complex simple. 

Jon Krohn: 00:00:42

Welcome back to the SuperData Science podcast. We’re lucky to have Andrew Jones with us on the show today. Andrew has held a number of senior data roles over the past decade, including at the tech giant Amazon. In those roles, Andrew interviewed hundreds upon hundreds of data scientists, leading him to create his Data Science Infinity educational program, a curriculum that provides you with the hard and soft skills you need to set yourself apart from other data scientists during the interview process. To give you the maximum benefit from this episode, I interrogate Andrew, so that he spills the beans on all of his data science interview secrets. Today’s episode will be of interest to anyone who’d like to improve their chances of success in interviews, probably with particular appeal to those in the early stages of their career. 

Jon Krohn: 00:01:38

Andrew Jones, welcome to the program. I’m so excited to have you on. Where in the world [inaudible 00:01:43]? 

Andrew Jones: 00:01:45

I am currently in a small town called Didcot, which is in Oxfordshire and the UK. I’ve I’ve just recently moved my family out there from London. So we had a two-bed apartment in the city of London and I’ve got two small girls who are age four and two, and they need a little bit more space than a two-bed apartment would allow them to stomp around and throw things. So we’ve moved out into Oxfordshire countryside where they can do all of that stuff as much as they like. So yeah, a bit of a change I’ve been here for about a month or two now but loving it. It’s definitely a change of lifestyle, but it’s very nice to have the freedom, especially in the last year that we’ve had. It’s nice to be out of London. 

Jon Krohn: 00:02:29

Yeah. I’m familiar with small town Oxfordshire in the UK. Having lived there myself for five years and it is absolutely beautiful, especially in the time of year that we are in right now. As we start to have that handful of sunny days that England gets every year when you can stand in the countryside with a pint of lager. I don’t know if there’s nothing more beautiful than that. 

Andrew Jones: 00:02:57

Yeah. We’ve had a couple of good days lately. In fact, been quite good, it’s maybe 24, 25 degrees Celsius today. So it’s quite warm but you’re right you don’t want to miss the sunny days you blink and you’ll miss them over here. And then there’ll be when winter before it again. 

Jon Krohn: 00:03:12

Yeah. It’s when those days happen, everyone kind of puts tools down. You don’t really expect much office activity. Like, we got it, it’s the day, let’s enjoy it. 

Andrew Jones: 00:03:21

Exactly. This is the day and people go over above and beyond. Monday everybody’s dressed up suit and ties and then Tuesday there’s a bit of sun, so people are out and they’re just in their shorts, I have permission to get semi-naked now because I’m British it’s sunny. 

Jon Krohn: 00:03:41

Nice. So, you are British and you’ve been living in the UK for a long time but your accent, Andrew does not sound totally British. What’s going on there? 

Andrew Jones: 00:03:52

Yes, technically I am British. Well, technically I’m a Dual citizen, so I’m originally from New Zealand and I was born there, I grew up there and I just moved over to London about, it must be 12 years ago now. A lot of Kiwis and Australians, what they do when they’ve finished university and maybe they’ve worked for a couple of years, they go on their overseas adventure and they explore the world and for a lot of people because London’s very similar cultural liter to home, it’s a nice, easy one. I actually have family here anyway, both my parents were born in Britain but obviously live in New Zealand. So it was a nice, easy, easy transition for me and I’ve just stayed. I mean, most New Zealanders that come over, have their two-year visa and then they’ll hit back because they’re forced to head back. 

Andrew Jones: 00:04:44

But I didn’t, I’ve got a British passport as well as a New Zealand passport, so I could stay and I just did and I met my wife here and I mean, she’s also a Kiwi, met her over here and we’ve got, like I said before, we got two young girls now and it’s home for us. But whether or not we go back to New Zealand at some point, I don’t know at the moment, we don’t really know where we want to be. We like it here. We’ve thought about maybe moving to Dubai because we love it there. Maybe back to New Zealand, we don’t know, I’m envious of these people who just know where they want to be and they buy their house and they set themselves up. We can’t do it. That’s a bit of a problem for us. 

Jon Krohn: 00:05:29

I don’t know it sounds like a not a bad problem to have, to have that flexibility and actually your job allows you to have, I guess, an infinite amount of flexibility in where you live because you run something called Data Science Infinity. You’re the creator of this data science curriculum. Tell us about Data Science Infinity. 

Andrew Jones: 00:05:48

That’s right. So, as a little bit of background, in my career and analytics and data science, I’ve been in the very fortunate position to have interviewed and screened hundreds of data science and analytical candidates. Over time, I started to see what it was that differentiated candidates that landed the role versus those who unfortunately missed out time and time again. I’d always wanted to create something like an online course, but I never really knew if the time was right. I was consulting it at Sony PlayStation up until about March of last year, so much of 2020. There were some tax changes in the UK that affected people who were consulting or contracting, and the bottom essentially fell out of that market and I thought this is the time to do it. 

Andrew Jones: 00:06:47

I’d been squirreling away bits of code that I thought were really useful in ways to explain them to people and different tools and techniques, which I knew were really valuable. Like I say from interviewing all of these candidates and seeing this process, I saw that there was definitely this disconnect between what students are generally learning in data science courses, whether that be online courses or whether that be at university and what hiring managers are actually looking for in practice, there was this misalignment between the two. And I thought, I’ve got this experience interviewing, I’ve seen exactly what it is that makes a candidate get the role. Maybe I can build something that will help people get there because it is such a struggle. We know how hard and how competitive data sciences, maybe I can help people in that way. 

Andrew Jones: 00:07:42

So I started out thinking, this is going to take me about two months to record the content and then about six or seven months later with no income, it was a risky move. Seven months of no income living in London was a dicey move, I would say. But seven months later I launched the first version of Data Science Infinity and it’s continuing to evolve over time. There’s four things that I think make Data Science Infinity different and I made it that way on purpose. So firstly, students are learning the right content. So, I didn’t want to just create a course, like a lot of other courses where it’s just the course creators opinion on this is what you should learn because I know it there’s not really the way I want it to go. 

Andrew Jones: 00:08:29

So not only had I seen people come and go in these interviews and I saw where the gaps were, I went out and I interviewed and talked to hundreds of leaders and hiring managers and recruiters as well in the field and asked them, what are the key skills that you need for data scientists and data analysts who are coming from learning until your teams and what are the ones that you don’t need so much that maybe get a little bit more attention than they should do, especially when people are starting out. So, I wanted to be very data-driven in terms of the curriculum that I was creating, and that includes softer skills as well. What are the types of things that people do that essentially make them a great data scientist versus maybe a good data scientist? Does that make sense? 

Andrew Jones: 00:09:15

I wanted the curriculum to be data-driven so people are learning the right skills and they can focus their time learning things that will actually help them get their first role. The second thing is it’s all about learning in the right way. So there’s this really heavy focus. I put a lot of almost pressure on myself when I’m creating the content to make it as intuitive and as understandable as possible. And everything’s based around project-based application as well. You learn the theory, but it’s applying that in real world scenarios as much as you can do in a course setting. Because I think those are the skills that help you get into the role because people want to see that you’ve actually used the tools and the skills to add business value, whether that’s even hypothetical because you’re using a data set of Kaggle or whatever it may be. 

Andrew Jones: 00:10:09

Then students evolve with the program. This is a big part of what I wanted to do was make it less about a destination. So, you see these data science, boot camps saying, learn data science in six weeks or become a data scientist in three months and I don’t believe that’s a thing. It might seem like a nice product to buy but in the real world I’ve seen these people come and go and interviews and it doesn’t work like that. So, I wanted this to be more of a journey rather than a destination. So, you get unlimited access to everything and the content will live on time. So I’m doing [crosstalk 00:10:48]. 

Jon Krohn: 00:10:48

That’s the company name, Data Science Infinity is based on that idea? 

Andrew Jones: 00:10:52

Data Science Infinity, yes. So, my wife came up with that actually when I was talking to her about how I wanted this to be positioned in the market and how I wanted it to be different to what other people were doing. So yeah, it’s very much about that. It’s an infinite journey, we’re all always learning, there’s no destination where I’ve become a data scientist. What should I do next? It’s always an evolution for whoever it is in the field, people at the cutting edge is still learning because things are moving so quickly. The other part of it, which if you want it, then you can opt for unlimited and dedicated guidance and support from me in your journey. I’m not saying that I know all the answers but I have worked at the types of companies that people want to work at. I’ve made all the mistakes that people make and I’ve learned from them and I can help people get to that stage. 

Andrew Jones: 00:11:45

Again, I’ve been in a very privileged position to have interviewed and screened all of these candidates and I can help you almost reposition yourself from one of the candidates who struggles to get the role and we know that’s a really common position people find themselves into one of the candidates that will get the attention of a recruiter or a hiring manager, and then succeed in the interview process because what a hiring manager is wanting to hear. So, I guess if I was to summarize that, a little bit of a ramble about it all, it’s really is about getting students results rather than just getting another certificate for your resume. If that makes sense. 

Jon Krohn: 00:12:28

That does make a lot of sense. I want to make sure that we somehow, so you mentioned as you started talking about the Data Science Infinity approach. You said that there were four things, but you only numerated the first two. So I want to make sure that we got them all, or I want to make sure I know what the numbers are. So the first one was, the curriculum covers what data scientists actually need to know as opposed to what the instructor happens to know and teach. The second thing was, learning intuitively, which makes a huge amount of sense to me. And then, so the third one that it’s this infinite journey and the fourth one is unlimited guidance. 

Andrew Jones: 00:13:03

Yeah, that’s right. So the third one is essentially the idea that the content will keep evolving over time. So as new things come into the market, or maybe certain skills become less required and maybe other skills come into replace them, then I’ll try and evolve the content. Like I say, I’m building up a big section on deep learning at the moment because not that that is essential knowledge for somebody coming into the field, but I think it is something that people want to learn. So that’s going to sit in the subsequent section of the course and almost going to tell people you’re not allowed to touch that until you’ve done the core concepts which things like sequel and Python, statistical concepts again, in a very intuitive way where you’re thinking about how you would use them, not just as a formula full of Greek letters, because that’s great if you know it like that. And I’m not begging people that know the maths so much better than I do. 

Andrew Jones: 00:13:59

But when you’re in a business setting as a data scientist, one of the big skills that you need is the ability to translate that for people who aren’t coming from a technical background, the people who are going to be the green light for your project, getting implemented or going on to effect customers and if you can’t explain that to them in a way that they can understand, then your product or the solution that you’ve built is just going to sit on a shelf. So that’s super important, that intuition that I’m trying to convey to people and then things like AB testing, these are the simple things but you need to know them really well because they’re what data scientists and data analysts just need to be doing. 

Andrew Jones: 00:14:40

Then there’s a big section on machine learning, which is again, all about understanding how it works from a hands-on point of view but then also understanding what goes on under the hood really intuitively in a way that you can maybe tweak it to work for different scenarios that you might come into contact with. Like something like logistic regression can be used in different way depending on the scenario, but you can’t do that if you don’t understand what’s going on under the hood. Then there’s a big section on softer skills, I guess, around turning business problems into data science solutions, which I think is super important. In my view, in my experiences, one of the things that is the difference between a good data scientist and a great data scientist. And then there is a big [crosstalk 00:15:27] yes, sorry. 

Jon Krohn: 00:15:30

You can finish that. And then I’ll go back. I was going to dig into some of those points, but it sounds like you have one more kind of big section to talk of. 

Andrew Jones: 00:15:37

The last section of the course, as it stands, is just a section around going from learning to getting the role that you want. So that’s where I’m sort of trying to get you to the level where you understand what hiring managers are looking for and you understand the types of things they want to see, and the types of things they don’t want to see in. And you can talk to them in a language which makes you very appealing to them. That was the only point I wanted to add. 

Jon Krohn: 00:16:03

Perfect. You may already have heard of DataScienceGO, which is the conference run in California by SuperData Science. And you may also have heard of DataScienceGO Virtual, the online conference we run several times per year. In order to help the SuperData Science community stay connected throughout the year from wherever you happen to be on this wacky giant rock called planet earth, we’ve now started running these virtual events every single month. You can find them a datasciencego.com/connect. They’re absolutely free. You can sign up at any time. And then once a month, we run an event where you will get to hear from a speaker, engage in a panel discussion, or an industry expert Q and A session. 

Jon Krohn: 00:16:48

Critically, there are also speed networking sessions where you can meet like-minded data scientists from around the globe. This is a great way to stay up-to-date with industry trends, hear the latest from amazing speakers, meet peers, exchange details, and stay in touch with the community. So once again, these events run monthly. You can sign up@datasciencego.com/connect. I’d love to connect with you there. 

Jon Krohn: 00:17:16

So we are going to dig into a lot of these topics in more detail, very shortly. So, things around what soft skills data scientists need to have aside from just the technical skills, talking about what hiring managers look for, we’ll get into all of that. But first, before we dig into that which I know our audience really wants to hear about, I also want to just bring up how you have a book that is also related to all of this. I think we’ll be very interesting for our listeners. So the book is called, Essential A.I. & Data Science Handbook for Recruitment. I guess I missed the word the, at the beginning, it should have said, The Essential A.I. & Data Science Handbook for Recruitment. So that book came out in early 2020. So just a little over a year ago. I guess that is related to a lot of the curriculum and philosophy that you have in Data Science Infinity? 

Andrew Jones: 00:18:15

It is but I guess it’s for a different audience. So Data Science Infinity is really tailored towards people who are looking to get into the field themselves. Whereas this book was written specifically for people who are hiring talent in data science and within the big umbrella that we call AI. I mean, this book came about, I was sitting having lunch with the recruitment agent that actually got me connected with the guys at Sony PlayStation because we used to catch up every time he’d come to London. We were talking off the cuff around the types of things that I was working on and he made a comment around, I wish I knew more about this stuff not just because the stuff that we do in data science is kind of interesting. 

Andrew Jones: 00:19:05

He said, “If I knew slightly more about this stuff, it would help me so much in my role, understanding the job descriptions a little bit more closely and on the other side, understanding the resumes or the CVs that I’m seeing.” Because they are the middleman between the job description and a resume. And they’re trying to effectively and efficiently connect the dots in a really accurate way. I just thought maybe there’s something in this. So yeah, I mean, the book is really aimed at people who are looking to recruit talent in the field without maybe going into all of the gory details of it. 

Andrew Jones: 00:19:44

So, recruitment agents or people in HR, or even maybe senior level managers who aren’t specifically technical themselves, but they may be in a role like the head of data or whatever it may be, but they are not a data scientist themselves but knowing a little bit more about the differences between programming languages or algorithms that are commonly used, could be really helpful when they’re trying to build up a team. But like you say, there was definitely a crossover between my emphasis on intuition. So this book is even more that way inclined, this had to be super easy for anyone to pick up because it is genuinely directed at an audience who aren’t technical and the reason they want to get it is because they don’t quite understand it though. 

Andrew Jones: 00:20:33

And the reality is they’re not doing it day in, day out, their role isn’t sitting there coding. It is trying to connect the dots between these things. So that’s where that book came from. And that was a really nice to have written a book because I’d never done that before. I don’t know if I will do it again anytime soon. I think I enjoy creating courses a little bit more than writing a book. I think it was so much work trying to get it into a format that I was happy with. I know you’ve written a book and that’s in a somewhat similar vein Jon, but it is a lot of work definitely. 

Jon Krohn: 00:21:09

Yeah. My book is a lot more like your Data Science Infinity content than your book designed for recruiters in that it’s a kind of an intuitive, I definitely focus on intuitive visual ways of understanding deep learning concepts and deep learning illustrated, but don’t need to talk about my stuff, let’s focus on you Andrew. So with your understanding of what recruiters are looking for, having yourself interviewed hundreds, thousands of people for data science roles, having spoken to lots of hiring managers, recruiters to come up with content for your Data Science Infinity curriculum, how can aspiring data scientists set themselves apart? 

Andrew Jones: 00:21:56

Well, I think there are a lot of ways that you can try and answer that question, but what I kind of like to do, and it alludes to what we’ve been talking about already is trying to give advice that might be the simplest to put in place but might have the biggest impact. And it’s a super competitive marketplace. We know that data science roles are being… Every data science role has at least 100 applicants. And that’s not going away anytime soon, even though where maybe the data science market is maturing slightly. I think people are starting to understand what it is and what it is and the little bit more than maybe three years ago. I think there are things you can do, which are very simple and very quick in terms of getting yourself ahead of other candidates. 

Andrew Jones: 00:22:45

This is a huge part of what I spend my time with the students in Data Science Infinity. But I think as a candidate you are essentially looking to showcase your skills at all times when you’re looking to get a role, during the hiring process, it’s subtly in different ways. So whether it be on your resume or whether it’s the projects in your portfolio or whether it’s actually when you’re sitting in that interview chair, you’re trying to showcase your skills. My overall kind of piece of advice, and I’ll dig into it a little bit deeper in a second, is that don’t think of all of those three things and how you showcase them as different, think of them as all being part of one representation of you and the work that you’ve done. Just either at a really summarize level like you might have on your resume in much more detail in your projects or when you’re talking about it in an interview. 

Andrew Jones: 00:23:41

I think the best piece of advice I could give would be at a holistic level. So, when you’re interviewing essentially what you are doing for the vast majority of it is, discussing the impact that projects that you’ve worked on the past have had. So when you’re preparing for going through the interview process or the hiring process, try and rework all of the projects that you may end up talking about into, and this is really simple stuff, everyone knows this, but I think I can’t overemphasize how important it is, rework them all into the STAR format. And I’ll talk exactly what the STAR format is. Most people have heard of the STAR format. I’ll explain in a little bit more detail for anyone who hasn’t heard of it but it is such a simple thing you can do, but it will affect your resume, your portfolio and your interviews, and they’ll all become this connected representation of you. And it makes you showcasing exactly what you can deliver so much easier for you. And that takes the stress off you in that process and allows you to run through things, especially in an interview and a really flee free flowing narrative. And that’s exactly what hiring managers really want to see. 

Jon Krohn: 00:25:00

I think I remember what STAR stands for, we had in my undergrad, if you want it to get any positions in the undergraduate student administration. So to like to lead a club, to get funding for your club, they were like, make sure every answer that you have follows the STAR format, and then you got formally evaluated based on that. If I remember correctly, it’s situation, ask, action, oh, no, reflection? 

Andrew Jones: 00:25:33

Maybe in some. The one that I use is results. 

Jon Krohn: 00:25:37

Result. 

Andrew Jones: 00:25:38

Results. 

Jon Krohn: 00:25:40

That makes sense. 

Andrew Jones: 00:25:41

For me, I mean, and like I say exactly when you sit and you sketch this out it’s nothing complex. You can do this so easily, but it will make such a big difference. So exactly like you say, answers situation in a data science world, this is essentially for each of your projects. This is the context around the business problem and why it needed to be solved. I guess it really pulls the interviewer or the reader, if it’s one of your projects into your narrative and it gives you a foundation to then go into the more technical details without losing them, without them not really understanding what you’re doing, because it’s the reality of the hiring processes. If it’s a recruiter or a hiring manager, they are quite fickle about things being made too hard for them. 

Andrew Jones: 00:26:30

If your portfolio project is just a bunch of code and you’re like, this is a life-changing piece of code but the hiring manager can’t see it because you’ve not explained it to them and it’s going to take them half a day to dig through it themselves when they’ve got all of their day-to-day stuff to do in their role, it goes in the too hard basket. That’s not a perfect world, but it’s the reality. So yeah, it’s situation and that’s essentially you giving some context around the business problems, why it needed to be solved. And then you’ve got task exactly Jon, and this is pretty quick and easy. It’s essentially just what was your specific role in the project? Super simple but important. Not every project we all know is just one person doing everything and it can come across quite poorly if you try and claim that you did. 

Andrew Jones: 00:27:17

It can come across quite well if you say, this is what I did, and this is how I supported other parts of the project, that’s quite nice for people to hear. They can see how you understand what the different roles within a project are. So, that’s quite easy that won’t take much of your time. A is action and this is the specific actions that you took. So this is going to be where most of your aunt’s is going to lie. For each project, when you’re preparing for an interview, try and refine this all down to a really succinct and compelling narrative as much as you can but keep some supplementary context up your sleeve. So not only I did this, I did this, and then I did that, and that was the end of it. Why did you choose solution C over solution B and solution A because a good interview will ask you that. 

Andrew Jones: 00:28:06

So having prepared for that kind of a question is a really good thing to have, and it showcases that you’ve got this broader knowledge of what you’re doing. So if you chose a certain type of machine learning model, that’s great, but what were the pros and cons from a technical point of view, what the pros and cons from a business point of view. Those are the sorts of things that when a hiring manager hears that they can see that not only is this person solved that problem, but I can see that they’re going to come in and solve any problem that I give them because I’ve got a system in place. And that’s what you need to show a hiring manager, because the chances are, unless you’re in a really specialized field, that you’re not going to have solved the exact problems that that company has because you’re moving from a retail company to I don’t know a financial company and the skills and the tools are kind of similar, but you’ve probably not solved that before. 

Andrew Jones: 00:28:56

But if you can show, show that you’ve got a system in which you can critically think about why you’re solving it and what might work well, and what might not, that’s like gold dust for our hiring manager. And then R is results finishing it off. This is super, super important. It’s often either missed or under emphasized by candidates. You really just want to show the impact of the work or the project, what was the impact that that had because that’s a nice way to show why you did all of this hard work. I guess it subconsciously starts moving the cogs in the hiring manager’s brain around the value that you can add and therefore, maybe this person can come in and make me look good or make the business money. And those are KPIs, which they’re thinking about in the back of their head. 

Andrew Jones: 00:29:46

I guess one final thing on there, try and use tangible figures where possible. So it drove a 100,000 pounds or dollars in sales, or it saved this many hours of analyst time per week, that really gives some color to it. It’s about the subconscious checkboxes in the hiring manager’s brain, and you’re starting to hit them all if you can do these things. Then there’s one more really, really good thing Whenever you’re preparing, for each of these projects, just ask yourself, if I was to start this project again, now, what would I do differently? That sort of thinking again, if you’ve just prepaid for this and you’ve had a bit of a think about it for 10 minutes and you’ve jotted a couple of ideas down that’s all the effort it takes. That can be so impactful because it shows that not only have you got win us oof the business impact of your project, it shows that you understand the nuance of what it is that you do from a technical point of view. 

Andrew Jones: 00:30:43

It shows a growth mindset because you’re thinking, well, I did this project six months ago and now I’ve learned all of these other things because that’s what we do. We just keep learning. So now, I would have done that differently and that doesn’t have to be from a technical perspective. It’s not like I would have used this different algorithm. It could be from a people management point of view, like one of the tricky things about that project was trying to sell that into a stakeholder. Since then, I’ve learned about this way of doing that. And again, that is so powerful for a hiring manager who’s wanting to see somebody come in and solve the problems that they have. Like I mentioned before, all of these facets, so your resume, your portfolio and when you’re in the interview itself, I briefly mentioned that I was trying to get across the point of trying to think about these things all as connected rather than think about them in isolation. 

Andrew Jones: 00:31:35

When you’ve prepared for a documented to each of your projects using something as simple as the STAR format, you just simplify that down for your resume. So, per project, you have one sentence for the context of what the business problem was. One sentence for the actions you took and one sentence for the results. And that becomes really easy because you just extract that from your STAR format that you’ve written down and then your portfolio, you can structure that in the same way and it gives a really nice narrative. So people can come in and quickly see exactly what it was you did. And then when you’re sitting in the interview chair, the words just fall out of your mouth in this really, really clear narrative. 

Andrew Jones: 00:32:19

You’re trying to get ahead of the interview almost, you’re trying to make sure they don’t have any really good probing questions that are going to throw you off because you’ve essentially covered them all within reason. So, that’s my kind of high level piece of advice. I see it as really simple, anybody can do it, but trust me, it will make you stand out from other candidates who really either on their resume, they just shout about their skills or in an interview. They just try and tell you the things they know, not the things that they’ve done and the value they’ve added. 

Jon Krohn: 00:32:52

Yup. Makes perfect sense. I think I had a brainwave, as you were speaking. So after you talked about situation, task, action, result, then you described talking about reflecting on the situation and things you might’ve done differently. So, that’s how I got my R wrong. I said reflection because we had the way that they spelled STAR was with two Rs. So it was this a situation, task, action, result, reflection. 

Andrew Jones: 00:33:20

There you go. Yeah. Well, that’s exactly the same, isn’t it? As a side note, somebody I worked with it with Amazon, her name’s GG she is now, she’s at Lyft Amazon. She’s now known as the Amazon interview whizz. And that one around, what would I do differently? That is something that comes from her training. So her and I interviewed over 100 candidates at Amazon. And she interviewed like 350 or 400 candidates. And that one of her key things that differentiates a good interviewee versus one that doesn’t quite make the cut, that one simple thought process around growth mindset. It can really make a difference and you should check out her YouTube channel too. It’s probably the best interview advice you can get. It’s focused towards Amazon, but you can use it anywhere. 

Jon Krohn: 00:34:09

How can people find out what’s her full name, GG… 

Andrew Jones: 00:34:12

GG Gallagher, and her YouTube channel is just called the Amazon Interview Whizz. If you search for that on YouTube, you should come across it. But if you’re looking for a role, whether it’s in data science or whether it’s in just the broader field then look it up. You’ll definitely find some good stuff there. 

Jon Krohn: 00:34:32

Nice. We’ll make sure that that is in the show notes. Nice. So, outside of being able to set themselves apart with this star process, being able to demonstrate their technical skills, what else should data scientists have in their skillset? I think that this is going to be interesting. This is a recurring topic. So, I even tried to summarize this kind of idea of what sets apart a good from a great data scientist. It tends to be softer skills. I summarized those ideas in episode 466. I would love to hear what you have to add. 

Andrew Jones: 00:35:12

Yeah. I mean, I’m glad that you’ve asked that question too, because in my view, I agree completely with what you’ve said. I think it definitely is a differentiator between what I would say a good data scientist is and a great data scientist is. For anybody that’s looking to get into data scientist, it is so important to emphasize and I guess to understand that data science is not all about the technical skills. I would say that in my experience, working with other data scientists in my career, I would say that the best data scientists are not the smartest ones by the definitions by what we say smart people are, they do know their stuff in terms of coding or statistical concepts, or other key data concepts and machine learning algorithms. They know those things absolutely don’t get me wrong. 

Andrew Jones: 00:36:07

What differentiates them from the rest is that they understand what the business problem is in its truest sense. Or I guess another way of thinking about that would be that they understand what the business is trying to achieve, whether that’s at a project level or at a broader level. And then they use data and they use their unique skillset because let’s face it as a data scientist. You do have a unique skillset, which other people in the business don’t have. They use that in very clever, and to be honest, often simple ways to solve the problems or to add tangible value to the team or the business, or to the end user or customer. 

Andrew Jones: 00:36:48

It’s not all about complexity and it’s about starting with the business problem and working back to a data science solution, if that makes sense, not the other way around. That’s something I wish I’d learned earlier in my career. I would say good communication skills are vital. I mean, that’s quite a broad area communication- 

Jon Krohn: 00:37:11

Number one that we hear when I ask data science hiring managers on the show, what are you looking for? [inaudible 00:37:22] number one thing is. 

Andrew Jones: 00:37:25

Yeah. That’s again, and I’m not trying to plug the Data Science Infinity at the moment, but I’m trying to get that across when I’m teaching people, it’s not just about knowing lots of statistical concepts. It’s about knowing how to use them and how to explain them to stakeholders in the business who are probably not as technical as you are. If you don’t have a good system of doing that, then you’re not going to get your projects across the line and you’re not going to add any value. Adding value is the one thing you’re going to do over and over again, that gets you the promotion, gets you to move up to a better job in another company. It’s all about… Because like I was just saying that when talking about how to showcase your skills, it’s not about saying, I know this, and I know this it’s about saying, and showing, check out the value that I added or check out the results I got from using this concept because people don’t really care what they want to know how you’ve used it. 

Andrew Jones: 00:38:22

But you’re coming back to good and great data scientists and I guess the difference between the two, I would say that, and communication has the broader topic. I would say a good data knows lots of technical concepts, but or maybe a great data scientist can simplify them down in a way that gets everyone in the business on board. So we can get this project across the line, whatever it may be, because at the end of the day, we’re here as data scientists to solve problems. We’re not here to introduce new ones and introduce complexity. And we’re here to accelerate business decision-making and help the business make better decisions faster not get in the way of it by trying to be super smart. Something I say to people all the time and in Data Science Infinity, try and get this concept across and make people understand how important these softer skills are, is that within reason nobody’s going to pay you to just be good at coding, or just to be good at math, or just to know lots of machine learning algorithms. But they will pay you, I guarantee they’ll pay you very, very well to add value to the business using your skills. There’s a subtle difference there and certain people coming into the field, not all of them, sometimes they get that balance wrong I think. So it is something to really keep your eye on when you’re up-skilling. 

Jon Krohn: 00:39:55

Beautifully said, Andrew and I couldn’t agree more. I think that all of the guidance you’ve provided people so far on positioning themselves on their resume and their interviews, I get a lot of questions from listeners who reach out to me on LinkedIn and say things like, “I keep getting rejected from interviews, what am I doing wrong?” I love that I can now point them to this episode in particular. I also had a recent episode, episode 480 is my top resume tips. So, we focused a lot now on primarily on interview ideas though everything you’re saying around using quantities to describe an impact that you’ve had and tying that impact to some commercial objective, you should be able to do that in each of the bullets on your resume as well, just as you can do it in a response to an interview question. So, yeah, so top resume tips episode 480, you can check it out for that specific resume stuff, but everything today, such brilliant guidance on interview preparation. When we were talking before the show Andrew, something that came up that I’d like to detail here, a specific data science career related question that I think you have a lot of insight into is, should data scientists be trying to be data engineers? What do you think about that? 

Andrew Jones: 00:41:30

Yeah. That’s an interesting one. Definitely. And it’s [crosstalk 00:41:33]. 

Jon Krohn: 00:41:32

We should define first, what is the data engineering? You should [inaudible 00:41:38]. 

Andrew Jones: 00:41:39

Yeah. I guess the difference would be… Well, let’s set this up by giving data sciences the baseline. I could probably more confidently talk about what data science is but I’ll do my best for data engineering as a comparison. I guess you could say that, so data science itself in its simplest terms is the process of making data useful or adding value using data, whereas data engineering is subtly different. It’s the process of making the data usable or integrating the data with the infrastructure that you have. So, a data scientist would probably spend most of their day staring at the data itself. Whereas the data engineer is probably going to spend some of their time staring at the data, as well as some of their time steering at the infrastructure which will hold that data which will move that data. 

Andrew Jones: 00:42:39

So, I know that’s a super high level generalization, but it is quite a heated topic, or it has been recently. You see posts around things like data engineering going to take over data science. I personally disagree with that because I don’t see them as being… It’s not a zero sum game. I see them as being two overlapping but complimentary parts of a bigger data picture. And there are other, so many other parts of the data picture as well. And this was the problem we saw maybe three to five years ago was that data science was seen as the solution to all of the data problems that our business had. So, people were hearing about data science, so they’d bring in data scientists, and then they’d be slightly disappointed when things weren’t moving along because the data scientists didn’t quite have that data engineering skillset, their set of skills was slightly more specific than that. 

Andrew Jones: 00:43:41

I was talking to somebody about this the other day, and it was a bit of a funny analogy, but I’ll try and remember what I was thinking. I was thinking, if you had an organization and your data team just had one data scientist, and that was it. That was your whole data team. Hypothetically, speaking, they might add 50 of these value points that I’ve just made up. Instead of a data scientist, you didn’t have a data scientist, you had one data engineer instead, I think they would add 50 of these hypothetical value points on their own. But if you instead had a team where you had one data engineer in one data scientist, you’re going to get more than 50 plus 50. You’re going to get so much more than 50 plus 50 because you’ve got these two skillsets that are overlapping but different enough that the data’s going to be coming in and it’s going to be prepared in the way that then the data scientists can really apply the things, which not only they are good at but the things they’re passionate about and the reason that they became a data scientist. 

Andrew Jones: 00:44:46

And that’s when you really start the flywheel spinning, it’s not about data engineering versus data science. It’s about how can we get the most out of our data team. I think those two roles are complimentary to each other. I also see a lot of posts at the moment saying that data scientists need to be data engineers as well, in a sense. So they need all of the skills that a data engineer has. I don’t necessarily agree with that fully. I don’t think there’s anything bad about a data scientist trying to upskill in those areas because they are overlapping, but a data scientist has got enough on their plate coming into the field doesn’t infinite ocean of things that you want to learn. And are we just saying that now you need to have two jobs, you need to have the skillset of two full roles in the organization. I don’t think so. I don’t think anybody’s got enough time in the day to do both of those things. 

Andrew Jones: 00:45:42

Well, at a commercial level, I think if again, going back to my own experiences, the best setups that have been in there has been a team of data scientists doing their thing, and a team of data engineers doing their thing. And they work side by side. They work together to make sure that they understand and they appreciate what the other one does because the data’s going to be flowing between them. They understand how to make the life of the other one easier, but I’m not in any way going to advocate the fact that if you’re a data scientist, you have to also have the full data engineering skillset. I think there are some awesome skills within data engineering but it’s not a bad idea to have, getting slightly closer to how some simple things within the cloud where within AWS or within Google cloud platform, for example, those are really important things to have but don’t think you need to know everything about data engineering. I’d say, I guess find which one you have more passion for and continue down that route, that would be my high level advice for people. Don’t put so much pressure on yourself thinking you’ve got to have the skillset of two full roles. 

Jon Krohn: 00:46:54

That makes perfect sense to me. Would you say it’s fair to, the a way that we could define the two different roles is, I guess if data scientists would typically be working with a static, let’s say you could have a file of data on your machine as a data scientist, and you could train a model with those data. And that’s the data scientists could have just this fixed set of data to train their models with. The data engineer on the other hand, is primarily concerned with the way that data flow in real time in a production system. And then of course, as you’re saying, having skills from either camp is going to be an asset to you, but you don’t need to feel like as a data scientist getting started, oh, before I can even apply to a data science job, I also am going to need to have these data engineering skills or vice versa. When people are considering, should I be preparing myself for data science career or data engineering career as you say, at the end there, a really great way to make that decision is which way do you gravitate more towards, towards building models with a fixed dataset or worrying about how you can efficiently flow data through? 

Andrew Jones: 00:48:15

I think you’ve explained the difference between the two in the way that I understand it. I think there are lots of different ways that people explain the differences because there are gray areas around them and they do overlap the two fields. But I think you’re right. If I’m thinking about what I was doing at PlayStation, I was a data scientist at PlayStation. I was doing exactly that. So the PlayStation data in reality is going to be firing in from the gameplay, which is happening. That telemetry is coming in all the time in all of its headiest form. A data engineer would be responsible for formatting that data and sending the right data through. So then some clever data science process or solution can be applied to that. 

Andrew Jones: 00:49:00

Then you’ve probably got if it was a machine learning solution, for example, then you’ve got a machine learning engineer which I think the general acceptance as the engineer which sits after the data scientist has done their work, they would try and put that in production. Again, these really big overlaps and these terms that we use for these roles are quite gray. But that’s kind of my understanding of it. And that is how I’ve seen it work best in organizations like Amazon and Sony PlayStation. I’m not saying that my view on this as the only way, and it’s evolving all the time but that’s my view from my experience. 

Jon Krohn: 00:49:41

Yeah, I like that. I agree, totally. I think that’s a really great way of at a high level of distinguishing these things. We could say, data engineer is concerned with production pipelines of data and getting all the right data into the right place. Then a data scientists can work with those data and can design a machine learning algorithm or some kind of analytics or some statistical model on top of those data. And then the machine learning engineer, can take the model that was developed and get that into a production system, maybe working with backend engineers who are even maybe a little further down that line. So, the machine learning engineers kind of they’re in the middle between the data scientist and the backend engineer anyway, all kinds of fluidity between those stages but I like those definitions. 

Jon Krohn: 00:50:30

So you’ve talked a lot at different points in this episode about organizational impact, that’s been a huge part of how you’ve been framing a lot of data science work in general. Obviously, you also have that that handbook for recruitments of also a business focus there. I want to specifically ask you based on that strong awareness of organizational impact of data science, how can organizations ensure that data science projects are a success? 

Andrew Jones: 00:51:09

Yeah. I mean, that’s a hot topic at the moment, or at least in the last six months or a year where these reports are coming out saying things like 85% of AI or data science solutions are ending up as failures. I see what they’re getting at. Obviously there’s a lot of wiggle room around a statement like that, what is AI? What is data science? What does failure? There is a lot of margin for error around a statistic like that, but I get the gist. I think the two things that come to mind for me and the first one is hopping back to what we just discussed. I think setting up the data team in the right way is important. Having the right people in the right roles, doing what they’re skilled at. 

Andrew Jones: 00:51:56

So, like we just mentioned not thinking that a data scientist should be able to solve all of your problems, but getting data engineers in, for example, to first get all of the data set up in a way that the data scientists can be let loose on it and can use their unique skillset to apply the magic of data science. That’s quite a broad one, but I think that’s important. I think we’ve seen some improvement in that over the last few years. I think the second one that I have seen a lot is that organizations or even data scientists themselves they sometimes look to jump to complexity too quickly. And this can be really dangerous from a project point of view. If you have a business problem that you want to solve and you think I know how I’m going to do that, I’m going to build some deep learning model and we’re going to implement in this way. 

Andrew Jones: 00:52:53

That can be dangerous. It’s not always but you can end up spending a huge amount of time and a huge amount of resource and a huge amount of money because data scientists and data engineers are not cheap. Building something that either drifts away slightly from the initial requirements because you’re trying to force in a solution that you think would be awesome to work on but it moves away from the initial business problem that you need to be solving. Or potentially, you spend six months building this complicated tool or solution and it turns out that it wasn’t quite as useful as you first thought it would be. You’ve spent all this money and you’ve spent all this time and you’ve not really got much for it. I think, if organizations and maybe data scientists themselves had a bit more of an MVP or a minimum viable product mindset that can be really, really useful. 

Andrew Jones: 00:53:47

So, the more simplistic approach of a minimum viable product, it means that you, there’s a couple of benefits that I can see. The first is that whatever you build it can more often than not. It can be easily understood by all of the key decision makers that need to be involved as well as the data science team knows what they need to do. The data engineering team knows what they have to do, the machine learning engineers know what they have to do, but then also the non-technical maybe managers or stakeholders they understand what’s going on because we’ve built something more simplistic than it potentially will become that everyone can get it. Everyone gets it on the ground floor would be another one, another way to think about it. Everyone gets in at that core understanding. 

Andrew Jones: 00:54:33

That means that you’ve essentially got this high probability of getting something into production or something into place that’s going to start adding value. It gives all of these teams a touchpoint, scaling up from there to a more complex version two, becomes much more easily because you’ve got everyone’s buy-in at that initial level. Having a more simplified version one, or the same MVB product, it doesn’t mean that you can’t have a version two, which has all the bells and whistles in the world. It just means you’re getting everyone in on the ground floor. You’re getting buy-in, you’re getting everyone understood on, is this going to work in the way we think it’s going to work? And if it doesn’t, if it’s not successful, then you’ve got there quite quickly that that can be equally valuable. You put something into play, you build it, you put it into production relatively quickly. And if it’s not as successful as you think, or maybe you get some key learnings from it, then the business can pivot or change direction really easily without that heavy investment of time and money. 

Andrew Jones: 00:55:39

I think that could be if people had that mindset of starting with an MVP, even if it’s just a sketch it up to start with, it doesn’t mean you have to put that in place, but just to start the thinking there, get everyone on board and then progress onto the complexities, add the things that you really want to add but start with the bare bones of it and get everyone understanding. That would be my, and that’s again from experience of seeing it and I’ve done it. I’ve wanted to… I’ve learned about this new deep learning technique that can do something, I just want to force it in there because I want to have that on my resume and it just doesn’t work a lot of the time. That would be my high level advice to people in data science organizations around maybe a way to have more success with your projects or your solutions. 

Jon Krohn: 00:56:26

Beautiful answer. So, you have been extremely enlightening in this episode. I think that listeners will come away from the episode with a clear understanding of how to set themselves apart from others, especially in an early stage data science interview, but at any stage in their career. And even if it’s in just related jobs or I suppose, any job. So, do you have any particular book recommendations maybe related to other than obviously your own book which by the way, where can we get copies of that? Where could somebody get The Essential A.I. & Data Science Handbook for Recruitment for [inaudible 00:57:09]? 

Andrew Jones: 00:57:10

I self-published it, so it’s available on Amazon and it’s available in the US and the UK and also in Europe I think as well. So yeah, if you just search for that very long title and you should find it. Yeah. It’s got a big yellow cover. You can’t miss it. 

Jon Krohn: 00:57:26

Nice. All right. So other than your own book maybe you have some recommendation for a book or maybe it could just be a more broadly a book recommendation that people would find interesting. 

Andrew Jones: 00:57:38

So, the one that I’d recommend at the moment is one that I have found myself going back to time and time again and actually more so at the moment because for Data Science Infinity, I’m halfway through building a whole new section on deep learning. For me, deep learning, I personally prefer, so obviously, the three kind of big ones you have, you’ve got TensorFlow, you’ve got PyTorch. And then on top of TensorFlow, you’ve got Keras as well. I personally like Keras the best. It sinks with me a little bit more. I know it’s a little bit more building block blocky than maybe PyTorches but I quite like it, and I’ve had no problem building out solutions using it. But anyway, the book that I really like on Keras just I’ve actually got it here because I’ve been referring to it, is called Deep Learning with Python it’s by François Chollet, who is the creator of the Keras Library. 

Andrew Jones: 00:58:33

He works for Google, I believe he still works for Google and he created it, but obviously he is an extremely clever guy, but the way he explains things visually and the way he almost like the story arc from starting of the concept, very intuitively and building into the more complicated pieces of knowledge that you need to know. I really liked the way he’s done that. I kind of take inspiration for that when I’m trying to think about building tutorials for my course as well that nice arc to get people this similar to what I was saying before, get people in on the ground floor and then help them move up. So, that is my book recommendation. I would say, if you want to get into deep learning, I think is a really good book because it’s nice and intuitive to start with, but you do get right into the complex topics and you see what’s going on under the hood as well which is quite useful. 

Jon Krohn: 00:59:27

Yeah. Keras would be my number one recommendation as well if you were are hands-on, more applied than really mathematically oriented data scientist, Keras is definitely the way to start learning about deep learning. So that’s a great recommendation. All right, Andrew, how can people follow you or get in touch with you? What’s the best way so that they can continue to get these insights the episode. 

Andrew Jones: 00:59:54

I’m only really on one social media platform and that’s LinkedIn. So you can find me [crosstalk 00:59:58]. Yeah. Obviously I have one of the most common names in the world, Andrew Jones. So you’re going to struggle with that a little bit, but my name on LinkedIn is Andrew Jones– Data Science Infinity. So hopefully that will help you find me. Definitely connect with me, I post every day about either data science or moving through your data science career. So, definitely connect with me and send me a message because I do sit on LinkedIn all day. That is my social media platform that I use and I love talking to people about data science. I have a YouTube channel, which I’ve started very recently and I’m going to start adding a lot more content this month. And that is just under my name, Andrew Jones. Again, it’s going to be a tricky one to find, but if you put Andrew joins data science infinity, you will probably find me a lot more easily. 

Andrew Jones: 01:00:47

And then for Data Science Infinity, if you want to know any more about Data Science Infinity, obviously get in touch with me on LinkedIn, just DM me, I will absolutely get back to everyone that DMs me. Or you can go to the main site, which is data-science-infinity.com. And on there, there’s all of the details around why the course is created in the way it is. There’s more about me and my background. There’s a lot of feedback from students. So if you’re unsure about is this for you or is this not for you, then that can help you there’s the full curriculum. So it’s got a list of all of the tutorials and all of the different modules and all of the different quizzes that are in the program at the moment and obviously more, more is coming. Like I said, I’m building a whole section on deep learning. I want to do something on DACA. I want to create some flashcards this year to help people study for interviews.

Andrew Jones: 01:01:40

I want to add two really cool projects this year. One using a genetic algorithm, which I think is just the coolest thing ever and one using the [inaudible 01:01:48] pathfinding algorithm. And those are two that they’re outside that absolutely necessary skillset, which Data Science Infinity’s all about, but they’re going to be projects that will get you noticed by hiring managers as well. So, visit the site. There’s a bunch of free content. You can watch a bunch of the tutorials for free anyway, and there’s an info pack you can download as well, which has got even more information. So yeah, data-science-infinity.com and you can find everything there. 

Jon Krohn: 01:02:15

Awesome. All right. Thank you so much, Andrew. It’s been such a treat to have you on the show and hopefully we can have you on again soon to share more insights on crushing data science. 

Andrew Jones: 01:02:28

I would love to come on again. Just let me know. Thank you very much, Jon, it’s been a pleasure. 

Jon Krohn: 01:02:39

Well, I told you I’d extract all of Andrew’s interview secrets. I must admit it wasn’t that hard. He seems eager to share them with the world. Some of the key points he covered were, the data scientists can set themselves apart by using the STAR situation, task, action, result and reflection approach on their resume. And especially during interviews that there’s no harm in developing engineering skills, if you’d like to be a data scientist, but those engineering skills aren’t strictly necessary. And how avoiding jumping to complexity too early is the key to successful data science projects. 

Jon Krohn: 01:03:18

As always, you can get all the show notes, including the transcript for this episode of the video recording, any materials mentioned on the show and the URL for Andrew’s LinkedIn profile as well as my own social media profiles at www.superdatascience.com/483, that’s www.superdatascience.com/483. If you enjoyed this episode, I’d of course greatly appreciate it if you left a review on your favorite podcasting app or on the SuperDataScience YouTube channel, where we have a video version of this episode. To let me know your thoughts on the episode, please do feel welcome to add me on LinkedIn or Twitter, and then tag me in a post to let me know your thoughts on this episode. Your feedback is huge for figuring out what topics we should cover next. 

Jon Krohn: 01:04:04

All right, thanks to Ivana, Jaime, Mario and JP on the SuperDataScience team for managing and producing another amazing episode today. Keep on rocking it out there folks, and I’m looking forward to enjoying another round of the SuperDataScience Podcast with you very soon.