SDS PODCAST

EPISODE 239:

FROM CANDIDATE TO CAREER:

PATHWAYS FOR

DATA SCIENTISTS

Kirill Eremenko: This is episode number 239, with Head of Analytics

Recruiting at IT Search, Adrian Clarke.

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.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.