SDS 239: From Candidate to Career: Pathways for Data Scientists

Kirill Eremenko: Welcome to the SuperDataScience Podcast. My name is Kirill Eremenko, Data Science Coach and Lifestyle Entrepreneur. And each week we bring you inspiring people and ideas to help you build your successful career in data science. Thanks for being here today. And now let’s make the complex simple.

Kirill Eremenko: Welcome back to the SuperDataScience Podcast, ladies and gentleman, boys and girls. Today I’m super pumped to have you on the show, because we’ve got one of the top data science recruiters joining us for this episode.

Kirill Eremenko: Adrian Clarke is a data science head hunter and head of analytics recruiting at IT Search, a company which specializes in recruitment globally. Adrian has hired for roles ranging from data science beginners, to data science practitioners, to data science managers, and even to top data science executives. And on this podcast you will get tons and tons of knowledge from Adrian.

Kirill Eremenko: We talked about things like the state of the data science industry globally. Different data science roles that exist in the world. Data science salaries and what to expect if you are getting hired, or if you’re hiring somebody. The gap in data science skills and why there is so much demand for data scientists. Data science in various industries. And very interesting concept of hybrid professional that Adrian talks about. How to hire or get hired as a data scientist. And lots and lots of other insights for your career, or your business if you’re a business owner.

Kirill Eremenko: So regardless of your level, whether you’re just starting out, or you’re a data science practitioner super happily employed, or you’re a business owner, or an executive looking to hire data scientists, this is a podcast where you will find those insights that you’ve been looking for.

Kirill Eremenko: I’m super excited for you to hear this episode, and without further ado, I bring to you, Adrian Clarke, Head of Analytics Recruitment at IT Search.

Kirill Eremenko: Welcome to the SuperDataScience Podcast, ladies and gentlemen. Super excited to have you on the show here today because calling in from Dublin we have Adrian Clarke. Adrian how are you going today?

Adrian Clarke: I am super Kirill, how are you?

Kirill Eremenko: Fantastic, fantastic. What time is it for you?

Adrian Clarke: It is currently five to seven on a beautiful Friday morning. What time is it there?

Kirill Eremenko: Amazing. Well for me it’s five to five evening, or afternoon also on a Friday afternoon. Thanks for waking up so early mate. It’s a great pleasure to have you. I’m super pumped. Like for our listeners, we just went through the list of topics that we might cover off this podcast. And all the things we want to talk about. It’s insane. Like I am really looking forward to chatting about these things. How about you Adrian?

Adrian Clarke: Absolutely, yeah. Really humbled to be asked to come on, and really enjoyed listening to your podcasts over the last week actually, in advance of this. And yeah I think we’ve a lot of ground to cover so hopefully we get some of the way, and if we don’t, maybe we could do it again. You never know.

Kirill Eremenko: Fantastic, sounds like a good plan. All right well to kick us off, can you give us a brief overview. Who is Adrian Clarke? And maybe a bit about your background.

Adrian Clarke: Sure, absolutely. So, oo Adrian Clarke. So, I was born in 1990, I’m 28.

Kirill Eremenko: Yep.

Adrian Clarke: There’s a good starting point. So I come from a place in the North West of Ireland, countryside upbring, place called Sligo. Grew up there, went to school there. Later then attended college in Trinity College in Dublin. And spent most of my adult life actually in Dublin thereafter. So, I would probably always see myself as a city boy, who was born in the country. If that’s a fair statement.

Adrian Clarke: I have worked in the world of talent acquisition, or recruitment, for six and a half years now. Something I don’t believe sometimes. And yeah, so based in Dublin. I live in the city center. I’m an outdoors kind of guy. So, health and fitness, key part for me. But also someone who is very loyal to my career. You know, I’m career driven kind of person. Very passionate about people, which is the reason why I suppose I’m in the world that I’m in. But also someone who’s incredibly social. Bit of a techy, not going to lie. And also someone who really loves the arts and culture.

Adrian Clarke: So, I think all of those things, that creative side, that professional side, and that kind of people orientated focus are all parts that make up who I am.

Kirill Eremenko: Fantastic. Well thanks for the brief overview. And you are Head of Analytics Recruitment for IT Search and Selection. Tell us a bit about the company. What does IT Search do?

Adrian Clarke: Sure. So, IT Search is effectively a technology talent partner, a global technology talent partner I should say as well. We’re based in Dublin city center. We’re part of a wider group of companies, which consists of five companies overall, across a variety of sectors. That’s finance, life sciences, construction and technology, as well as HR and life sciences innovation. We also have a capital markets business as well that we work on kind of classic finance.

Adrian Clarke: And IT Search really is the tech arm of all of that. So amongst our team, we have experts who are completely verticalized. Myself being focused on data science, analytics engineering, insights and digital analytics. I work with colleagues who specialize in software development and project management, as well as Microsoft technologies and various other development technologies.

Adrian Clarke: So we’re four years old. We are reasonably, we think, definitely innovative in what we do. In that IT Search is a global supplier of talent of course. Internationally. But we’re also consultative. So we represent candidates first, and work with really superb leadership, startup, innovative, and also really established companies as well, literally across the world. So we work across the European market. We work into Asia. We work into North America.

Adrian Clarke: And from time to time we’re asked to work on really interesting and unique projects in other surprising locations. And in those instances we’re quite happy to fly out. Because our USB is really one of those … Or sorry USB really is to be one of those business that, you know, doesn’t necessarily want to set up a multitude of offices across the world. What we’d rather do is be truly global, not be domiciled to one area or zone, so that we can represent the candidates we work with ubiquitously.

Adrian Clarke: And also from a commercial standpoint, be a truly nomadic business in 2019. I mean, our people can take a plane. We can work internationally. We’re at, what I like to call maybe the bold face of what talent acquisition should look like in 2019. Because the war for talent is on. And you need to be innovative, you need to be disruptive to be successful in this market. And that’s what we’re about.

Kirill Eremenko: Love it. Totally love it. We talked a bit about this before we started the recording. And this is so cool that even though you’re based in one location, you have access to a pool of talents. And you seek this access to a pool of talent throughout the whole world, throughout the globe. And you actually also help companies place and find talent throughout the globe, wherever they are.

Kirill Eremenko: So it’s really cool because that means what we’re going to be discussing today is not just a localized view of what’s happening in the world of data science and recruitment, head hunting for data scientists in Ireland and Dublin, this is a global world view. Would you say that’s quite accurate?

Adrian Clarke: Absolutely. Absolutely. And I think that’s the differential here in … We’re very fortunate to be, I would think, and maybe I’m a little bit bias in saying this, but we are one of the only true data science, data analytics talent partners in the Irish market. And you know, I’m surprised when I look at this. I mean, globally, even at an executive search level, the majority of organizations haven’t developed a data science or analytics practice. Maybe that’s down to a lack of knowledge in the space. Or maybe because the space is so fast changing, or ever evolving.

Adrian Clarke: Or maybe it’s because people aren’t passionate about it. And I am. So four and a half years, nearly four years … or sorry four years of pure data science analytics engineering head hunting, recruiting, you know, you learn a lot. And the candidates have taught me a lot. And it’s humbling, but it also gives me a very strong sense of duty as well, to represent those people properly. So in terms of your listeners, I realize that there’s people here who might be starting out their careers, people who are quite established, there’s people here who maybe interesting in hiring people for the first time and want to understand how they do that, why they should do that, and the challenges there in.

Adrian Clarke: But also it’s really important to kind of know that there are companies in the world who really know this, and specialize in this, and are expert in that. So if I can share a little bit of that today, and give people some confidence in finding a partner to help them with that, I’m happy with that of course. But also if there are people listening today who really are at a crossroads in understanding where they need to go or they’re trying to figure out what are the latest trends in data, or what they should be looking out for, hopefully we can help those guys too.

Kirill Eremenko: Awesome. Sounds like a great plan. Well to probably the next step for us would be, tell us a bit about what range of roles do you hire for. Like what are … Is it just mostly junior data scientists? Is it executive searches across the board? Just so we know what kind of experience … or the things that you’ve experienced throughout your career in the head hunting space.

Adrian Clarke: That’s a great question. So, broadly speaking, I cover a number of key themes. So data science is an absolute specialism of mine. I mean it’s something I love. And I get it. So when I sit down with candidates, you know, I speak technically. A little bit unusual. I mean, not a lot of recruiters tend to be specialized in understanding their market. I really love the technical standpoint, because most of my time is spent understanding candidates first, and secondly understanding and educating clients on the potential of that candidate base.

Adrian Clarke: So, some years ago I started talking about hybridization, and the hybrid roles in data. So, what I mean by that is, when we look at data analytics, coupling someones data skills, their capabilities in that space, be they a great data modeler, a segmentation style person, a marketing analytics person, or a … let’s say a statistician. You couple that skill set, that core capability, and you combine it with a domain. And in doing that, you then create a wonderful hybrid.

Adrian Clarke: So I often talk about one of the most difficult roles to fill in the world right now is a digital data scientist. This is someone who is a combination of data science, thinking, analysis, reporting, predictive analytics, who understands how digital technologies work. Be that programmatic technologies in the advertising space. So for example, why does Google or Facebook place an ad in a certain place at a certain time based on your traffic, based on your history? What about the sentiment of your search history and your personality connects to that from a data level, and generates a sense of your experience of that world at that personified view that you experience when you go on to your browser.

Adrian Clarke: So that for me is a digital data scientist. And the same could be applied to the world of insurance where you have … which would have been traditionally actuaries. And now most actuaries say to me, “I don’t want to be an actuary anymore. I don’t want to predict risk. I want to be a data scientist solving problems that are associated with risk.” So for example banking clients talking about risk data scientists, risk data analysts, not traditionally … or sorry what would have been traditionally known as quantitative analysts. So you’ve got that resurgence in roles now, where it’s about the tech and the knowledge combined, and that’s a hybrid.

Adrian Clarke: So broadly speaking I would work on data engineering. So classically a data engineer, a data scientist, a marketing analytics analyst, a data analytics developer which I think is a really interesting role to talk about because that’s a very nouveau role, which leans into the world of cloud and cloud technologies. I would often work on roles that have a digital slant as well as a dominise let’s say, or domainised slant. So, that could be a role like an ediscover analysts, so someone who’s combining the best of legal knowledge with text analysis, sentiment analysis. So going through documents utilizing data science with automation.

Adrian Clarke: So, it’s a really convoluted area to work in, in terms of hiring talent. Because if there is data, there’s definitely a role. But I suppose it’s up to me sometimes to help a company coin that role. You asked a good question there as well to [inaudible 00:13:19] my point, is it senior hiring, is it practitioner level, is it management level? It’s all levels really. Because, as I’ve said before, there’s a lack of specialists in the space in terms of senior hiring. We’ll often work on retainer with certain senior organizations to help them coin their first roles at senior level. Or top table as I like to call it. So that will be your chief analytics officers, your chief data officers.

Adrian Clarke: And that really goes right down to people who want to be very compartmentalized. So that could be someone who wants to work on a certain stack. That could be a data engineer who actually sees himself as a Scala developer, so they’re a Scala developer. But they’re all part of that data ecosystem, and they’re all part of whatever stack and over … let’s say overriding architecture that an organization is working on.

Kirill Eremenko: Wow. That’s very cool overview. And I totally admire … And it’s actually inspiring to hear that point of view of combining … like what you called a hybrid. Combining the data science knowledge plus where is this going to be placed. What kind of domain is that data science knowledge … does it need to be applied to. And what kind of data scientist can you hire for this company? Or what kind of data scientist can this person become? Very, very cool.

Kirill Eremenko: Tell us a bit more about the lack of specialists. So you mentioned there’s a lack of specialists across the board from junior data scientists to senior data scientists, to the top table as you called it. Why is there still a lack? Like data science has been around for probably a good ten years now. You’d think that by now there would be enough supply of data scientists who are able to fill these roles. What is your view on that?

Adrian Clarke: Sure. So I think there’s two ways of looking at that. The first part would be a simplistic view, okay, which I’ve had for many years, which is you’ve got great data people who are in the wrong role. So, my role then becomes informing them, or educating them on other ways, or other means by which they can apply their skills. So for example someone coming from the insurance sector in custom analytics, can easily apply that skill set to the world of gambling. You know, in a more digitally orientated domain. So-

Kirill Eremenko: And make money at the casino.

Adrian Clarke: So you know it’s a pretty vibrant sector. You know, and one [inaudible 00:15:31] as we know as well, to be fair to your listeners as well, as being regulated very, very well too.

Kirill Eremenko: Yeah.

Adrian Clarke: There’s good honest work in there as well. Like all things in life sometimes things need to be regulated and data has a role in that too in anomaly detection and protecting people. And you can probably touch on that again.

Adrian Clarke: The other way of looking at this as well in terms of a lack of talent, is simply to do with the rise in demand. You know it’s almost frightening Kirill. You know, organizations are realizing the potential in data. They know that there are pockets of excellence in their business if they just get their data under control. And when they really unearth the simple things, so they get their CRM in place, or they’re recording their data correctly on a BI level, they start realizing that there’s huge potential in predictive analytics. Or in combining indirect or kind of non relational data bases together. And that’s when the magic happens.

Adrian Clarke: The other way of looking at this as well, away from demand and people possibly being simplistically in the wrong role, is the lack of candidature coming out of colleges. I mean, universities are really doing some incredible things to increase the volume of graduates in data. But a lot of candidates who go into study data courses are being recruited out of college before they ever finish. Simply because their tech stack is so in demand, and in their minds a three or four year degree is too long. You know, it’s just simply too long because the pace of change is so great.

Adrian Clarke: So they’re either very lucky and move into a role with a great company who will give them all of the opportunity in the world to facilitate their knowledge and capability, and then they will go from there. Or do some contract work for a while. Or get involved in a startup. Or then join a global company. Or they will, I suppose, realize, and be a product of their success, and in their own right almost go back to their tech stack solely, like great programmers.

Adrian Clarke: And data science, you know, people talk about data science being the new oil. You know, data analytics in general is so in demand that colleges quite simply can’t keep up with the pace of applications in some cases. Intake classes are getting bigger and bigger. And as a result many of the universities are creating new courses which are there to attract people in, but simply maybe can’t fully support the extent of the skills and demand that needs to be there because the technology’s evolving at the same time.

Adrian Clarke: So in Ireland, one example university is the University of Limerick, which has created one of the worlds first courses in … a masters level course in artificial intelligence. And that’s divided up in terms of engineering and tech stack. It’s also divided up in terms of an entrepreneurial stack. And depending on the demands that organizations will have on those unique domains with the data skills in mind, will those students finish? Will they be available? Will the intact of classes coming in after that, even though it’s a new course and a pilot course, will they be successful by the time that the companies need change and the tech stack may have changed globally as well.

Adrian Clarke: So they’re the challenges we’re under right now in terms of that initial lack of talent.

Kirill Eremenko: Okay. Gotcha. And from a recruitment perspective, there’s a lot of listeners listening to this podcast, probably the vast majority, who are passionate about growth, self education and specifically doing that online. Online courses, online learning, online podcasts and so on.

Kirill Eremenko: What is your view as a head hunter, a recruiter about candidates who didn’t go and complete a masters of data science or artificial intelligence, but learned all those skills online? Do they have an equal chance of getting a placement in a job in this industry in data science?

Adrian Clarke: You know it’s a really interesting question. And it probably goes back to why your own following and your business is successful. You know, continuous professional development is an absolutely paramount and critical part of data, okay, as far as I’m concerned. You know, the first question I get from candidates is, “I’ve done these extra courses, should I put them on my CV? On my profile.”

Adrian Clarke: Absolutely you should. Not only is it evidence of your interest in the area, but it’s also evidence of having your finger on the pulse as it were. Companies really love that stuff. It’s essential. It’s added value. Because there’s no curriculum that’s going to cover every single part of simply how vast the world of data analytics, data engineering is. So it’s absolutely a prerequisite I think for people now in data to realize that working on projects outside of work, continuing to build other languages into your repertoire is essential.

Adrian Clarke: Companies need that. Companies love that. Do I feel that that makes up for a lack of maybe traditional academic training? You know, we’re living in a very different time now. I mean global organizations for example EY, some years ago said, “Well look, having a degree is no longer a requirement to come in and join their organizations.” That’s really promising for people that maybe have grown up in the world of data, or even technology in their bedroom. You know? Accessing different courses, accessing code, creating interesting projects.

Adrian Clarke: I mean, how many entrepreneurs, how many tech entrepreneurs do you know have set up businesses based on proprietary code that they’ve created? Or even data scientists who maybe won a Kaggle contest. You know created a phenomenal model. And you think about all of those companies who put on projects onto Kaggle for example and solve, some would say their greatest challenges in an open source capacity. Or a crowd sourcing capacity, in terms of data science knowledge.

Adrian Clarke: So, you know it’s an interesting question. Because a grounding is absolutely essential. I think having some tertiary education is really critical right now in the world of data. And it’s becoming a little bit easier for people because there are courses tailored to their skill set now, internationally. Which is really promising and we have to hand it to the universities that they are doing that. But also the technology institutes. They’ve become a really powerful place as well for this. Because they’re again on the pulse of innovation, seed capital. There’s that combination of commercial thinking and data capability and data techniques.

Adrian Clarke: So I do think, particularly for your listeners who may be looking at career changing or maybe looking at their first data roles, showing evidence of hands on application, showing evidence on your GitHub of some models you may have worked on, and showing kind of wider appreciation for a knowledge base or a specialism of your own, away from the norm, is really, really exciting and really, really important. So I’d encourage them not only to obviously listen to your podcast and obviously take in that soft skills element, but also to kind of really up skill day to day, and realize that that’s really powerful. Particularly where soft skills come in, as well as the hard skills.

Kirill Eremenko: Totally agree. And I’ve been like … I love that you mention that, because I’ve been saying this for I think over a year now, that guys listening to a podcast, if you want to be super successful and really boost your career, make yourself visible. Right. Like Adrian’s saying. Post your code on GitHub. If you’re using Tableau, learning Tableau go on Tableau public and post your dashboards.

Kirill Eremenko: Or course you know the caveat is don’t post any company sensitive information. But your own projects, your own interests. To show that you love this field, you’re passionate about it. So you’ve got GitHub, you’ve got Kaggle competitions, you’ve got Tableau public. You can go and just start writing about what you’re learning, what you’re discovering, what you’re experimenting with.

Kirill Eremenko: Just write a blog post. And you don’t even need a blog these days. You can publish them on Medium for free. Or you can publish them on LinkedIn. Very powerful tool. We had a guest here, Randy Lao, was about a year ago, he went from zero to … What was it? Like 40,000 followers on LinkedIn, within a year, just by publishing his learnings about data science and machine learning on LinkedIn as blog posts with images and code. And now I think he’s at 70,000 followers.

Kirill Eremenko: Because in doing that you’re not only making yourself visible to recruiters and companies, you’re also actually helping other people. So you’re doing a massive service. You’re like killing not just two birds, like three, four, five birds with one stone. And that is all at the price of what the cost of like two hours per day, or five hours per week of your time to just write up a blog post or post up some images online. It’s a massive, very powerful tool.

Kirill Eremenko: And Adrian, this is my view, that there’s a lot of demand, as you say there’s a lot of demand for data scientists right now, because companies … I love how you phrased it, that there’s these pockets that they can really get a lot of value out of in terms of … in the company there’s data pockets that if they tap into them properly, and get them organized, they can get a lot of value out of it.

Kirill Eremenko: And at the same time there’s a lot of … I find there’s a lot of data scientists. There’s a million people on Kaggle. Our courses have been taken by 700,000 people. And there’s plenty of data scientists who want to get into this profession. But the problem for recruiters and companies is that in this ocean of applicants, it’s really hard to find who’s actually going to stand out. Who’s actually going to bring value to the company. Like if you have … If there was like a shining beacon, or like a gem in this ocean of people in this crowd, you would jump at it. But unless there’s that beacon, unless there’s that gem, you can’t really tell. You have to go through thousands of applications. It’s a very tedious process.

Kirill Eremenko: So as long as you can make yourself stand out. And you can not just send a resume in, but actually say to a recruiter, like if somebody came to you Adrian and they said, “Hey Adrian, I want to jump in data science. By the way for the past year I’ve been posting all my learnings on data science on GitHub, and on LinkedIn, and I have 5,000 followers and I’ve helped 15 other people. I’ve mentored this one person. And I think … I only started in data science two years ago, but I think I’ve learned a lot and it’s all documented by the pet projects I’ve been doing, which I totally love and I’m passionate about.” Like how would you feel about that?

Adrian Clarke: Absolutely. I mean I kind of equate this to the creative world. So if you were a content developer, you were an artist, you’re a videographer, you know, you put your work out there. Because you’re proud of it. You give people another way and another vehicle of communicating with them and reaching them, and hitting them emotionally perhaps with their work.

Adrian Clarke: So I often say to people, “Have you got a portfolio? Okay. Have you got a website? Maybe you’ve got something that communicates a little bit more about the essence about what your passion is.” Really love this. For years and years and years I’ve always been someone who will always create a website, or I’ll always have an updated LinkedIn page, or I’ll use Twitter, or I’ll use other vehicles and means to just communicate with the base that I really want to engage with. Because meaningful conversations are what create meaningful opportunities.

Adrian Clarke: And that also applies to peoples applications, their roles and careers. Also as you said, getting the attention of a recruiter. And it depends of course on what country or domain you’re in. Naturally recruiters are going to reach out to you, or head hunters are going to head hunt you if they can find you. If they can’t find you however, and if you don’t necessarily want to be found, and there is a segment of people out there who are … and I’m sure they’re nodding now going, “Yeah I get a lot of calls and I get a lot of messages. And a lot of them are irrelevant.”

Adrian Clarke: And if you’re one of those people what I encourage you to do however, is yes, have you’re content base, maybe make it a little bit more difficult to be found. But be public in terms of communicating with your audience. So go to events. You know, how many meet ups are there around the world. I go to meetups all of the time here in Dublin, and across Ireland. And talk to data people. But more importantly identify people that are passionate by the tone by which they present their knowledge, their findings. And how willing they are to share and collaborate with other people.

Adrian Clarke: So, you’ve got to remember that it’s not only about the digital assets or online resources and all those things. We live in a H2H world. And that’s a little bit of hopefully a profound sounding point, I don’t know. But in a world where we’re so busy being socially engaged and constantly barraged with messages and engagements, and you know, everyone is on their phones all of the time. Sometimes you need to peel it back. Speak to people, go to an event. If that tickles your fancy and you’re going to meet new people and get excited about that, that might be a place where a recruiter will find you.

Adrian Clarke: I spend a lot of time in those environments. I found and engaged with some wonderful people. If I even think of the week ahead, even from a simply commercial standpoint, I’m meeting someone in a global consultancy who I met at one of those events, and I had just seen that that person had moved into a really interesting role, and they’re looking to hire.

Adrian Clarke: So it works in all sorts of vehicles and means. But I do think, and I have to agree with you Kirill on this, that being a little bit more collaborative, and sharing and adding your piece to the world, adding to that global repository of data, data sets, and kind of interesting means and vehicles to solve problems, is really important because you’re adding to what we’re doing as a data community.

Adrian Clarke: You know, we’re solving problems, we’re changing the world, we’re getting excited about the early stages, still the early stages and what the potential of data analytics, data science, data engineering is. And being part of that is sharing that. And also putting yourself out there. And yes, I’ll be very honest, it helps you get found. It also helps you differentiate yourself when you apply for a role. And it’s also just the story telling element. Because I spend a lot of my time telling people stories. So talking to data scientist, and hearing what they’re interested in, what they do.

Adrian Clarke: And then just as a pocket, an aside, they’ll say, “Oh well, I’m working on remodeling something, and it’s really interesting.”

Adrian Clarke: And I’m like, “Oh no, no. Tell me more about this.” Because we maybe just have a client who’s looking to do exactly that, but they’re pulling their hair out because they haven’t been able to find that solution through even some of their global tech partners. You know, so you have to kind of market your nuggets of difference, and your value, and realize your value, and be passionate about it. And also be willing to share it.

Adrian Clarke: Of course, as long as you protect your IP, and you do it in all of the very creative ways that we need to incorporate when we do those things. But more importantly that you’re careful about the decisions you make. And of course obviously you work with the right companies who are going to foster those ideas, and not necessarily take them away from you.

Kirill Eremenko: Totally. And for … I want to just make sure we’re not excluding anybody out of our listeners. In case you’re not looking to find a new job. In case you’re happily employed. Which is totally like the dream right. You’re happily employed, you’re not looking for opportunities, and you might think that all this is not really relevant to you, why would you put yourself out there, and share content, or go to meetups and so on.

Kirill Eremenko: It’s still really cool to network and build a network of data scientists. Because you might not be looking right now for a new opportunity, you might be looking later. Or even if that’s still not the case, if you’re super happy with where you’re working, which is again, the dream, I really wish that to everybody. In that case, by doing this you’re still attracting that network, and maybe you’ll help your company get some visibility, or maybe you’ll help your company find new talent to join your team and show the world that wow, these are such real cool, interesting projects.

Kirill Eremenko: Or maybe you’ll just meet somebody who’ll give you some new ideas. Like going to these meetups is not always just about getting a new job, sometimes you go there and you find new ways of doing things, new ideas of doing things. Or you share something online, in your GitHub or somewhere like on LinkedIn, and then somebody might say, “Hey, that’s a really cool way of solving this problem. I’ve also solved it, and I took this approach.” Like you learn new things through, as you said Adrian, through this community. We’re part of a community. So it’s very important to interact with it.

Adrian Clarke: Absolutely. And I think about how you and I connected for example. You had some phenomenal posts. A number of people had liked it on LinkedIn I think was the platform. You know, I thought, “I have to comment on this, I have to share this, because this is really, really interesting, relevant material.” And we connected thereafter, and now we’re obviously in the middle of a conversation.

Adrian Clarke: So there’s nothing wrong with putting yourself out there. I mean if you don’t put yourself out there sometimes you’ve kind of closed yourself off classically to a world of opportunity. And I would take your point again in terms of your base maybe that aren’t looking at for roles, or it’s specifically about their careers, you just never know who you’re going to meet, and you never know. Because there’s seven billion people in the world. That’s a big data set. Right, let’s be real about that. And human factors are, you know, if you’re going to create opportunity, you’re going to enlighten your world or change up how you see the world, the more conversations you’re going to have are going to really enlighten that.

Kirill Eremenko: Awesome. That’s a really good point and I think at this stage we’re going to shift gears a little bit, and we’ll talk about a question everybody’s interested in, salaries. Salaries and remuneration. And I’d love to hear it from you, because you are in the space. This is what you do for a living. You place people and make sure they’re getting paid right, and the companies are paying right.

Kirill Eremenko: What are … Like we all hear about these crazy astronomical figures that data scientists, or some data scientists are making upwards of $200,000 per year. What are the real numbers? If you of course can disclose these, share these. On average, what do data scientists make? Data science engineer, data science analyst, developers, executives and so on. What can you share in this space?

Adrian Clarke: Sure. I can tell you that it’s varied. Okay. That’s the most politically correct statement I can make, okay. So some of your followers are based in the U.S. market, internationally. I work a lot in the European market, and Middle Eastern market as well. So, it always goes back to the niche that someones working in, and the demand for that talent at any one time. That’s ubiquitous, I mean that’s across the world in any part of tech.

Adrian Clarke: You know, the first question I get asked is how much is too much? From a client perspective. So sometimes it’s important to kind of put yourself into those peoples shoes for a second and say, “Well look, we’ve got a budget, we’ve got a big project we need to deliver. Talent are powering that because talent is a critical cost and it’s the driver.”

Adrian Clarke: So I always start at practitioner level, or people who are starting out let’s say. In the European market it’s completely standard for people to start at in around 35, 40,000 euro, okay. If we’re using euro as an equivalent here for a second. We can maybe come back with some figures on this later on, or follow through with a sheet of comparison let’s say on this Kirill.

Adrian Clarke: So as a starting point that’s quite a good starting salary. Because executive roles, or when I say exec roles, are generous entry level roles let’s say starting out, traditionally have been paid at the lower end. So some people have grown up in roles, and they’ve worked really hard at the start, and they’re happy to be there. Or they’re interning. And that’s shifting. Companies realize that they have to pay a really solid starting salary as a statement of their interest in those individuals.

Adrian Clarke: Also driving salaries is this element of retention. Data people are moving faster than they ever moved before. And when I say move, I mean their loyalty to organizations. So they’re starting at … They could be starting on a huge figure, but it’s not the main driver for why they stay in the business.

Adrian Clarke: So when I talk to companies, and when candidates talk to me about what they should be paying, and what they should be earning respectively, it goes back to, is the project interesting. Or do we think it’s interesting, and then as a result do we think that the candidates that we’re going to represent are going to be interested in this particular project.

Adrian Clarke: So from the entry level up we’re talking about the 30 to 40 thousand euro mark. Where it gets really interesting is where people have three to four years experience under their belts. Or they have a domain of expertise that they’re really passionate about. That’s when people start moving into really astronomical figures. In the East Coast of the U.S. market for example, if we touch on there. It’s a demographic thing, it’s a regional thing. Salaries are often paid higher in New York, for various reasons.

Adrian Clarke: And then if we go to Silicon, okay, we have a totally different viewpoint. We have a standpoint that’s based on innovation, creating unique IP in data. So it’s very much about fostering new ideas, fostering effectively very commercially valuable material. Okay. And that has a price. Because keeping that in a business needs to be rewarded effectively to keep people in the business at all.

Adrian Clarke: So you know, the variances in those salaries in the U.S. market are vast, but it is largely regionally dependent. What is unique however, is this whole theme that’s been around for a couple of years, that if someone has come from, we touched on this earlier, a top tier university, a globally respected university, that they should be paid these incredible salaries. Maybe that suits the brands, maybe that suits those universities as fee generating businesses. Okay. Let’s be honest about this and be frank about it.

Adrian Clarke: Or is it about the caliber of the talent that they’re producing? And if those people are as good as they are and in many cases are that good, they’re deserving of those salaries. So at the senior end things get a little bit more complex, because we’re rewarding for experience, we’re rewarding for knowledge of multiple domains and technical expertise as well as the ability to drive a vision. And to imagine better ways of incorporating data into the business to impact on the ORI and the bottom line.

Adrian Clarke: So senior executive salaries in data are climbing at an alarming rate. And I say alarming because organizations simply don’t know what they should pay. And I mean that. So I’ve looked at some recent mandates recently where we’ve been retained. For example when I say retained, we’re paid in advance to really orchestrate these searches. Because they’re complex, and they’re really globally orientated. And at top table, senior professionals are drawing in equity, they’re expecting bonuses, they’re expecting hearty senior salaries. But they’re also expecting various perks and accoutrements to ensure that they’re going to stay with the business longer term. And I think the core of that is a trust in their vision. Because their roles are often quite disruptive.

Adrian Clarke: If you’re are a CAO or a Chief Analytics Officer, or a CAIO, a Chief Artificial Intelligence Officer, incorporating deep learning technologies, and technologies that will potentially automate jobs, you know, you damn sure better be very good at articulating that vision without effectively annoying people that have been in the roles a very, very long time.

Adrian Clarke: So knowing how to communicate the value of data, as well as recognizing the value of people therein in that vision are critical. And as such, we’re looking for very, very unique people who present more than just leadership characteristics. They’re phenomenal communicators. They’re also phenomenal change masters. And they’re expecting, in some cases, up to and beyond six figures. When I say six figures I mean the very high end of the six figures. And in some cases with packages all combined, the company go into seven figure salaries to deliver the level of performance. Depending on size of organization of course Kirill.

Adrian Clarke: So that’s a reality that companies need to be aware of, and need to know that it’s really happening. And for those at the kind of middle point or maybe people who aren’t in the world of data at all, but are interested in data, when they’re looking at business, and when they’re looking at other people in the changing role of data in businesses, they need to be cognizant of how that impacts on other peoples salaries as well.

Kirill Eremenko: Okay. Well, that’s a very good … Can we rewind back a little bit. So entry level, about 35 to 40,000 euros. Somebody with three, four years experience you said it’s regional … for instance in the U.S. East Coast different to Silicon Valley. Do you have any figures there? Just so that we can have a rough ball park. What can somebody expect with a … three to four years experience?

Adrian Clarke: Yeah absolutely. So, particularly at the U.S. market it’s very easy to estimate that it’s anywhere between 80 to 140,000 dollars. You know, with various perks. When I say perks I mean that’s obviously your health care, and your plans of course, but also bonuses are very common now in data. Because there’s a commercial quotient associated with the success of data models. And companies are now measuring that in the bottom line. They’re going, well how much of this came from really clever models, very clever use of analytics. Because that’s going back into their spend in the year ahead.

Adrian Clarke: And then from a European standpoint again, practitioners again at the three, four years experience mark are probably looking at 60 to 100,000 euro. It’s just equivalence. You know, salaries are higher in the U.S. by nature, it’s just the way that the economic systems structured. But I have seen data professionals with a very small amount of experience getting into the high end of the 60s the 90s, in the U.S. market with two to three years experience.

Adrian Clarke: It just depends again on how valuable their skill set is. Is it niche? Is it in an area that is up and coming? Is it an area where there’s a huge amount of disruptions? So for example computer vision. Or autonomous driving. Or in areas like deep learning, again, AI. Which I still go back to say deep learning, because I think we’re getting there, but I don’t think we’re at the AI point just yet.

Adrian Clarke: But again those people who are disrupting and doing the R and D in those areas in particular, can expect higher salaries, because they are being rewarded of course for their academic knowledge, but also the research perspective. And those again in other guises of data roles, so for example the data engagement managers, the data engagement executives, people who understand data but aren’t hands on practitioners, are moving into interesting roles as well. Because they’re connecting the business as well as the knowledge of data. And they have to be rewarded. So that’s somewhere between 50 and 120,000 dollars as well with three to four years experience as well.

Adrian Clarke: So salaries are rising, you know. And it’s not an open check book scenario. Okay, which is the phrase I’ve heard from some HR professionals. But there is a realism about keeping people in the business and getting what you’re due. What I say to people time and time again, it’s not solely about that. I mean you have to be there for the project, you need to be looking for variety, and you need to be sure that companies are buying into you and what your potential is. And perhaps also rewarding you for the time that you’re spending away from the core projects. That you’re adding more value to other areas of the business as well.

Adrian Clarke: So it’s okay to be demanding, okay. But it’s also very important to recognize that these are businesses at the end of the day. People are sharing profit. People are sharing all of this value together, and people are creating innovation. And that in its own right is hard to measure. But people need to realize also that at the end of the day there are business owners, there are corporate entities, and the success of the models and the design, and the engineering, the architecture that these people bring in has an impact on the long term capability and vision of those businesses. And effectively their jobs.

Kirill Eremenko: Gotcha. Very, very cool overview. And what I wanted to go from here towards is that I like what you said about it’s not just about the salary, right. It’s about the different projects that you’ll be working on. It’s also about the opportunities that you will have with that company. And we already touched on the chief analytics officer, the chief AI officer, there’s also roles like chief data officer, chief data scientist, and others in that space.

Kirill Eremenko: So can you tell us about what you’ve seen in terms of careers of data scientist. What are the possible routes that a data scientist’s career might take them, and the choices that they might be faced with along the way transitioning from introductory level data scientist, to practitioner, to data science manager, and to data science executive. What are your comments on that?

Adrian Clarke: Yeah, so it’s something that I’ve really watched over the years actually. At times, I sit down and I try and write a matrix for how these roles evolve. I would have worked with many companies who say to me, “Okay, we want to promote someone internally, you’ve placed this really great person with us. And we’re trying to decide whether they’re management material. Or they’re leadership material, or maybe they’re happy to drive projects and mentor other people.”

Adrian Clarke: So, there is an emerging let’s say career progression path in data science, think you touched on it there. So people will start as maybe a junior data scientist, or a data science developer. Evolve into a data scientist, a senior data scientist, a principle data scientist, maybe a data science manager, and then from therein move into the role of let’s say either a principle data scientist from a commercial standpoint, or a chief data scientist from an ownership standpoint. And when I say ownership I mean the tech stack as well as the vision of the products, as well as the autonomy given to that particular unit, or scope of the business where data’s concerned in terms of productisation or innovation in their particular domain field.

Adrian Clarke: So that is one projectory avenue. In terms of where people go from the roles in data science or their initial early roles, I mean it’s vast. One thing I can’t deny at the moment, and it’s a topic that I think some of your listeners may have heard or may be involved in that conversation, I’m sure you’re aware of yourself Kirill, is I suppose the absorbing of data science into data engineering. Because we’re seeing a huge amount of technological advancement in the way that models can be run off shelf. Or various kind of mundane tasks can be automated using data engineering. As a result there are new roles being coined therein.

Adrian Clarke: So you know, software analytics developers, data engineering developers, data science engineers, and those roles are really nouveau, and you’re going to see a kind of movement of people who would have classically been in the statistical world, or more quantitative world let’s say, or the quantitative sciences, or decision sciences, as some people call it as well, depending on the markets you’re in. Moving into more hybridized data guises. So combining the best of data engineering, data engineering automation processes, and data engineering principles, into their data science works.

Adrian Clarke: Or in inverse, seeing some data engineering practitioners moving their knowledge into data science. Because in many, many cases, data engineers are building platforms and systems and warehousing projects and cloud projects that support the needs of data science solutions. And that can work as I said, in inverse. It just depends on how you want to utilize your skills in a maverick way.

Adrian Clarke: But I do generally have to say … or sorry what I find is generally a theme at the moment, is that rush towards data engineering. It maybe that organizations are still moving from [inaudible 00:46:24] to cloud, I think that’s still very much a reality. And that may just be topical for right now or the next six months. But definitely with the incredible work that’s happening in terms of Google cloud, and I suppose just the scale of data that companies are working on as they grow out their platforms, we’re going to see a lot more focus on how data engineering and data science are interlaced.

Adrian Clarke: Another kind of interesting point as well, just on data engineering, something I’ve noticed over the last while is a lot of data architects, people who are on that kind of design piece, visionary piece in terms of the programmatics in tech, particularly in data, are moving back to more hands on development as well. Because I think the general theme there, and certainly from people I interview, they say, “Well look, I want to be more hands on, I want to be in the heart of the action. I don’t want to be looking at it at a high level.”

Adrian Clarke: So, the future going forward is an interesting one from the perspective of where these roles will be. I think that pod mentality is certainly evolving here again, where we have a knowledge expert coming with the domain knowledge of their organizational guys. Let’s say an insurance professional, or a HR analytics leader, or a HR professional let’s say, coming in, fronting a project, a data engineer, a data scientist and a data analyst all working on the data end and the technical side. And that person guiding the conversation with the actual needs of the business.

Adrian Clarke: So, definitely we’re going to find a lot more of that close collaboration, because the lines are blurring in terms of the techniques. But we will need people to kind of step in with a pure specialism. When I say pure specialism, someone who really knows their stuff in one domain, and is quite a purist about that.

Adrian Clarke: So an AI developer, is that a role now? I think it is. You know, people who understand how to work with deep learning, understand the technologies behind that, and are really passionate about the applications of it. But those are people who have seen beyond what we’re doing right now, and they’re are disruptors, and of course they’re trying to push the business forward and utilize new technology really, really fast.

Adrian Clarke: And again those people will find themselves having to norm with other people. Working very closely. And their own internal view of their role will change. And I think that’s certainly one of the key things that’s part of it now in data. That we’re morphing a little bit into new roles all of the time. And I think it’s really important as a head hunter, and a recruiter, to help people to realize that it’s no longer just about being titlist. You won’t always be a data scientist. That’s okay. You won’t always be a data engineer. That’s okay.

Adrian Clarke: But you may morph into a role that you never thought you’d do before. You might find that you like leading people. You might find that you like fronting the business, you may find that you want to be an entrepreneur, and you’ll step back from hands on and you’ll be more technically orientated towards the technical sale.

Adrian Clarke: So it’s fast, but again people need to trust that it will change and have a little bit of faith in the evolving role of what is the modern data professional.

Kirill Eremenko: Yes that’s a very accurate overview. We actually at Deloitte when I worked there, people could progress through their careers naturally as an analyst, then you go to senior analyst, then you go to manager, then you go to director, and from director, that’s where you need to start working with clients, making sales, and then from there you go to partner, and there you become … that’s all you do, you sell to clients.

Kirill Eremenko: And so some people wouldn’t be interested in that. Some people wanted to continue doing the technical side of things. And so therefore, instead of going from manager to director, they had the option of going from manager to principle. And principle was like a working director. Somebody who’s actually in the tools all the time.

Kirill Eremenko: So even large companies like Deloitte recognizes that it’s not for everybody, and therefore following Adrian’s advice here, it’s important to structure your own career. What do you actually want? It’s okay to try things out, but don’t force yourself to be a manager, or a leader of people if that’s not something that you’re passionate about. If you just want to do the technical side of things, that’s totally also okay, and you can grow, you just need to communicate that to your own manager, to the people that are running that business to show them that, “Hey, I can add most value in this way, or in that way.” That’s what it’s all about at the end of the day. Not conforming to certain boxes.

Kirill Eremenko: So yeah, that’s where your career can take you. Thank you so much Adrian for the overview. We talked quite a bit about different AI, engineering, technology roles. How important are soft skills in all of this? Like from what you are seeing. And I’d really be interested to get your professional opinion on this because from my observations, the highest paid data scientist, the most in demand data scientists, are not just the virtuosos of coding and algorithms and people who are really passionate and dedicated to the technological aspects, the hard skills. That’s very important. But the most successful and highest paid data scientists, are the ones that can actually bridge the gap between the insights and the business decision makers. So those who can not only derive the insights, but who can communicate those insights to the people who need to hear them. What are your thoughts on that? How important are soft skills?

Adrian Clarke: You know, when I hear that question, what immediately comes to mind is the evolving role of IQ versus EQ. So when I think about soft skills in businesses I think of the change quotient, or EQ. You know, how capable are people of adapting, moving in a direction that suits based on the environment that they’ve got, the various people they’ve got in the business. And being able to I suppose push the business forward, or the organization forward with those subtle qualifiers in place, or not in place as the case may be.

Adrian Clarke: So, soft skills are absolutely paramount. Particularly now at a time when organizations and people are becoming a little bit more cognizant of H2H interactions. I think I touched on this earlier. We live in a very social world. We’re on our phones. It’s a simple line, but it’s effective.

Adrian Clarke: So getting people into the room. Being tactile. You know, engaging with people, talking to people, presenting ideas, helping other people to communicate their own ideas. You know, how often is it the case that you’ve got the smartest guy in the room but he just can’t … guy or girl in the room I should say, and they can’t say or communicate what it is that they’re thinking.

Adrian Clarke: So being a great enabler I think is the critical point with soft skills now. And being cognizant and aware of where you sit in the team, where you sit in a conversation. And that applies to people who are listening that aren’t necessarily working in a data role. Also the ability to recognize, not only I suppose your value, but recognize other peoples value. And your value systems. And morphing or being a little bit of a chameleon. You have to do things sometimes in the world of business, in the world of organizational design, that aren’t necessarily comfortable for you, but are going to really elevate other people and push them forward, and as a result push the project forward.

Adrian Clarke: So when we talk about soft skills, communications for certain, yes presenting, talking to other people, communicating and articulating your argument, or your point in a way that’s ubiquitous. Applicable to most audiences.

Adrian Clarke: Also being able to technically explain. And when I say technically explain, you know, often times in data and analytics, engineering, we’ve got really complex undertakings or complex projects. Your audience is not always going to be a technical person. So you need to be able to tell those stories. And I talk about great story tellers all the time. You know, as a recruiter as a head hunter I tell people stories every day, and yes they’re often very technical. You know, how do I walk into a room and talk to an audience that doesn’t necessarily understand what the latest Kaggle contest model is, or even the concept of Kaggle sometimes. It’s not to say that these audiences don’t understand that. But sometimes they just simply don’t. It’s not the world that they’re working in day to day.

Adrian Clarke: So, it’s very important for data professionals to undertake courses, continuous professional development, like that which you have yourself Kirill in your business. And to kind of look at things in a different way. Also look at their tone, look at the way they’re articulating their point. They’re under a lot of pressure to deliver something. But, you know, take time to step back, look at things in the bigger view, in a world view.

Adrian Clarke: And also when we talk about soft skills, I mean, influencing. Influencing that you have the right point, influencing other people that they have the right point. Sometimes telling people that they’re wrong. And being wrong is part of life, it’s part of business, it’s part of organizational designs again. But you need to know how to approach those things.

Adrian Clarke: And also conflict. Now conflict is a critical point. Because we’re at a time of change. Organizations are designing what systems, platforms, technological advances to utilize. Data professionals are coming in with superb ideas, but often very disruptive ideas that change how businesses have been structured and worked for years, and years, and years. So there may be times when someones going to come to you and say, “I don’t really like that. Here is why.” And you have to learn, and you have to know, and at least have the means to handle the variables and control the variables, and come back in a considered way with a rebuttal, effectively. And know how to dilute situations of conflict in to positive outcomes.

Adrian Clarke: You know, they sound like very simple things when we do them every day. But there are better ways of doing it. And there are people who are expert in those areas. So I highly encourage people to recognize those things.

Adrian Clarke: You made the point Kirill as well, about senior people not being necessarily the virtuosos. Maybe it’s because they know how to speak to a greater cohort of people and communicate those things to a greater cohort of people. You know, I think so. I think that if you are someone who’s conscientious, if you can train yourself … and bear in mind it’s important that you can train yourself. These are skills by the way. You can train yourself to be better at approaching situations, and appealing to more people. But obviously you don’t want to dilute your sense of your personal brand and your opinion.

Adrian Clarke: So anyway I think, for soft skills to come into focus you need to consider, what are you trying to achieve? What courses may be very relevant for you and very relevant for the majority of your team let’s say. Or your colleagues. Or people maybe in your day to day life. Because you can apply a lot of these things to your private life as well as your commercial life as well. And then you need to take an action and create a plan. So sit down with yourself, make a list of things that you want to improve on. Self assess. And then slowly apply the things you’ve learned into your work environment, or your day to day life, and see what comes back. And I promise people, I promise them right now, that if they take a small change, incorporate it, work with it for a little while, work on those soft skills, they will see dividends. They will definitely see a return. And it will advance their career. It certainly will.

Kirill Eremenko: Fantastic. Thank you so much Adrian. That … We’ll wrap up on that. Very, very insightful notes. Thank you so much for coming on the show and sharing all these insights with our audience. Before I let you go, can you please tell us, what’s the best way for people to get in touch and connect with you?

Adrian Clarke: Sure. So I’m a … I think I said in an earlier conversation with yourself Kirill, you know, being out and about, being engaged, being socially engaged and using all the wonderful social platforms we’ve got now is really paramount for people.

Adrian Clarke: I’m a LinkedIn aficionado, so I’ve always used LinkedIn since the very early days of when LinkedIn has been there. But you can always reach out to me there. So I’m on adrianclarke1 is my username there on LinkedIn. And IT Search, we’re obviously based here in Dublin, so you can always reach us at itsearch.ie. And always my email is adrian.clarke@itsearch.ie if anyone wants to reach out for a little bit of advice, or you’re looking at maybe your next role. Or they’re looking at maybe setting up in Dublin and want to hire some people as well. You can use any of those channels of course.

Adrian Clarke: And I’m always willing to engage and network with this community. Because we’re in it together. And let’s keep the conversations going. And I thank you very, very much Kirill for being one of those beacons in a very busy data universe. And producing great content. And inviting people in to give their opinions. Opinions are great. And I look forward to people sharing theirs with me as well.

Kirill Eremenko: Fantastic. And I want to say thank you to you as well. Like, you’re also sharing tons of great content. For our listeners, Adrian’s got almost 24,000 followers on LinkedIn. This is crazy man. That’s huge, huge. Congrats man.

Kirill Eremenko: One more thing before we wrap up, finish off. Is there a book that you can recommend to our listeners to help them through their careers and on their paths to success?

Adrian Clarke: You know, you asked me earlier about this and all throughout our conversations I’ve been thinking on this. And I think of a book that I read many years ago. I spent some time in the Middle East markets when I worked in oil and gas energy, well before I ever got involved in the world of data. And I saw those people, I saw those organizations utilizing data to effectively reduce extraction and see that change. And that’s actually what inspired me to get into data analytics really. Because I saw the power of it. And the decision to come back to Ireland at the time actually was driven by reading a book by a phenomenal guy called Jack Canfield, okay some people nodding already going, “Yeah, I know Jack Canfield.”

Adrian Clarke: But there’s a wonderful book called Chicken Soup for the Soul. And it’s one of those books where you really short cut everything. He summarizes some of the best self help books in the world. Okay, there’s nothing wrong with self-help, like soft skills. And he takes the best of that knowledge, and he summarizes it, he breaks it down, and he leaves you with these wonderful, useful tidbits that really help you to kind of crystallize your thinking, or thoughts.

Adrian Clarke: And I sat down with that book and I did what he said, you know, read it three or four times, make a plan, and put things into action. And I really recommend that book for people that may be taking their first step into self development. Okay. Or particularly for data professionals, or those starting out their careers, or even those at top table. It’s a great book to focus your mind on you for a minute. Because we live in a busy world, where people want from you, and you know your constantly busy. And your phones going, and you’re on social, and I use that line again. You know, it’s okay to take a minute for yourself and read something that’s really, really for you. And remember that you have a place in the world, and you need to focus that energy.

Adrian Clarke: So Chicken Soup for the Soul, Jack Canfield. He’s a phenomenal guy. Listen to some of his work as well if you can. He talks a lot of sense.

Kirill Eremenko: Fantastic. That was Adrian Clarke, ladies and gentlemen. Thank you so much Adrian for coming on the show. And I’ll talk to you soon.

Adrian Clarke: Thank you Kirill. Thank you very, very much. And thank you for listening guys.

Kirill Eremenko: So there you have it ladies and gentlemen, that was Adrian Clarke from IT Search. Head of their analytics recruitment. I hope you enjoyed this conversation as much as I did. My personal biggest takeaway was probably the whole notion of the hybrid professional that Adrian talks about. And that is very interesting about how to find the right domain for your data science skills.

Kirill Eremenko: As we know data science skills are very highly transferable. And if you are proficient in data science in for instance healthcare, you could take those skills and very quickly get up to speed in another industry. For instance finance. And it’s very important … and I think a lot of times people don’t consider that their skills, or their interests might be better aligned with a certain industry than another. So that whole notion of a hybrid professional that Adrian mentioned on the show was very exciting to me.

Kirill Eremenko: And that’s not to say that there wasn’t any other insights, there was plenty of very powerful and useful insights in this episode. So hopefully you got them all down.

Kirill Eremenko: And on that note as usual you can get all the show notes for this episode at SuperDataScience.com/239. That’s www.www.superdatascience.com/239. There you’ll also find the URL for Adrian’s LinkedIn and Twitter. Make sure to connect with Adrian. Adrian has over 23,000 followers. You want to be listening to Adrian. You want to be getting those insights, and you definitely want him in your network. And needless to say if you’re a business owner, or you have a startup, or you’re an executive at an enterprise and you’re looking to hire data scientists, Adrian is your guy to go to.

Kirill Eremenko: On that note, thank you so much for being here today. And I look forward to seeing you back here next time. Until then, happy analyzing.