sds podcast episode 179 with matt corey€¦ · kirill eremenko: this is episode number 179 with...

Show Notes: http://www.superdatascience.com/179 1

SDS PODCAST

EPISODE 179

WITH

MATT COREY

http://www.superdatascience.com/179


Kirill Eremenko: This is episode number 179 with Data Science

Recruiter, Matt Corey. Welcome to the Super Data

Science 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.

Welcome back to the Super Data Science Podcast,

ladies and gentlemen. Super excited to have you on

the show today. And we've got a very interesting and

insightful guest joining us. Matt Corey is a data

science recruiter. And what I found very interesting

about Matt was that he actually specializes only in

data science recruiting, specifically just that niche.

And that's what we were talking about in this episode.

You will find out many interesting tips and insights

into what recruiters look for when finding candidates

for a data science position, you will understand what

to kind of expect from recruiters.

Also on the other hand, Matt will share some insights

on how he works with his clients, the companies that

are hiring. And you'll understand more about their

thinking, what are they looking for, what are their

fears, what are their desires, what is driving them. And

moreover, we'll talk about the intricate role of a good

recruiter in data science. Not just the person who puts

people in positions, but a person who acts as a bridge

between the candidates and the clients. A person who

works with expectations of clients, because we all

know that data science hasn't been around for that



long, and yet a lot of companies have huge

expectations. They're looking for unicorns, they're

looking for people with 10 years in data science

experience, and lots of different tools and techniques

and methodologies, and industry knowledge, which

just physically don't exist. And so Matt will share his

insights on how he goes about those situations, and

how he works with the clients themselves to manage

their expectations.

So if you are looking to hire data scientists, this

episode is also going to be valuable for you. And

finally, Matt has just recently published a book. You

can buy it on Amazon. When we were recording the

podcast, only the ebook version was available. But

when this is gonna go live, probably the hard copy's

gonna be available as well. It's called, The Data

Scientist's Book of Quotes. And I can't wait to get my

hands on that book, because it's got some very

valuable quotes. It's got over 300 quotes in there,

categorized by different areas of data science and

different topics. So I'm looking forward to getting that

as well. And we'll talk about the book and some, he'll

share some insights from there too. So on that note,

can't wait for you to check out this episode. Without

further ado, I bring to you Matt Corey, a data science

recruiter. Welcome ladies and gentlemen to the Super

Data Science Podcast. Today we've got a very exciting

guest on the show, Matt Corey. Welcome, Matt. How

are you doing today?

Matt Corey: I'm fine, Kirill. Thank you so much for inviting me. It's

a real pleasure.



Kirill Eremenko: The pleasure's all mine. Matt, where are you calling in

from today?

Matt Corey: I'm calling from beautiful London.

Kirill Eremenko: Amazing. And you-

Matt Corey: [crosstalk 00:03:42] warm at the moment, about-

Kirill Eremenko: You-

Matt Corey: ... 30 degrees.

Kirill Eremenko: That's fantastic. 30 degrees Celsius?

Matt Corey: 30 degrees Celsius, yes.

Kirill Eremenko: Just for our U.S. listeners, that's ... I should find out.

I'll find out what is in Fahrenheit. Which is 86 degrees

Fahrenheit. Quite a lot for London. Quick question,

you mentioned that it's been warm for quite a while

now. And you've been in London for 20 years. How

warm has it been before ... How long has it been warm

for now?

Matt Corey: It's been warm ... Yeah, it's a great question. Thank

you. Yeah, it's been warm now for about almost a

month. And I'm talking about maybe one day where it

rained possibly, like a couple of days sort of in the

evening. But in general, it's been a good month of just

solid sun, really.

Kirill Eremenko: Fantastic. That is totally, totally fantastic. The first

time I went to London was last year, I got there, first

day it was sunny. And I thought, "What is everybody

talking about? Why the rain, the bad weather? It

seems lovely." But then on the second day that's when

the rain started, and it was like four times in the day it



was raining. So yeah, I'm a bit ... I'm actually very

excited for you right now that it's such a good time of

the year.

Matt Corey: Yeah, no. It's amazing. It's kind of expected, because

it's obviously the sorta Summer period. But it's not

always like that. And this is really really a treat this

year. So I'm talking about sorta climate change and all

that, it is happening. And it is a lot warmer now than

ever. I mean this is, we're in London, and it's kind of

Mediterranean weather.

Kirill Eremenko: Yeah.

Matt Corey: So we're blessed.

Kirill Eremenko: Yeah. Okay. Yeah, something to be concerned about as

well, I guess. Well okay, so Matt, you are a data

scientist recruitment provider. You're an advisor,

speaker, and now a book author. And we'll talk about

that in a second. Tell us-

Matt Corey: Thank you.

Kirill Eremenko: ... quickly from a high-level perspective, what do you

do as a data scientist recruitment provider?

Matt Corey: Wow well, very simply I ... First of all, it is a niche. It is

only data scientists that I provide to clients and

organizations. So it is exclusively data scientists unlike

others who choose to do the whole sorta data science

sort of portfolio in terms of analysts and engineers,

and architects. I felt that there was a real importance

and need for data scientists to have that very sort of

special role in terms of providing the insights. And I

think that with over the years what I've seen is that



more and more, it's a role is gonna take a sort of

predominant role within changing business, and

providing sustainability. And also really being able to

maximize the data that is already inherent within

organizations. So that's why I chose to only focus on

data scientists.

Kirill Eremenko: Mm-hmm (affirmative). Gotcha. And how long have

you been doing that for?

Matt Corey: It's been a little bit under a year.

Kirill Eremenko: Mm-hmm (affirmative). Okay. So you've been helping

data scientists get roles in the past year. And where

did you come from into this space? Where was ...

Where were you recruited?

Matt Corey: So my background is within HR, Human Resources.

And I started off my career as a generalist HR person.

Then focused within recruitment. And at some point I

then became an independent contractor. And there

were a few changes in the market, and I decided to set

up my own recruitment practice. And I initially started

off within change and transformation. But within as I

mentioned, about almost a year ago now, I felt that

that was a bit too broad. And I wanted to really focus

on, and zero in on one certain position that was so

very very important. And I had seen a film called

Money Ball, which you might've seen with Brad Pitt.

Kirill Eremenko: Mm-hmm (affirmative).

Matt Corey: And there were certain things that were sort of ... That

were coming up into my life, and seeing the film, and

then reading a few articles. And then suddenly it was

like, "Wow. Data. Data science. That's the real change.



That's really what's happening." And then I then just

dived and just read as much as I could, ask people

about it, and just eventually just set it up as a data

scientist recruitment practice.

Kirill Eremenko: Mm-hmm (affirmative). Gotcha. And so how's it been

going? You've been doing it ... The whole

transformation and change you've been doing for quite

a while now. But the data science part that you've

been doing for the past year, how's that been going?

Have you been able to help many people?

Matt Corey: Yeah, I have helped many people. Either in terms of

placements, or in terms of advice, or in terms of

helping them with their CVs. Get a lot of people from

across the globe. My LinkedIn connections have just

sort of skyrocketed. I'm currently doing a promotion

for as a sort of Summer promotion for one person to

get a free CV and sort of LinkedIn profile rewrite. And

it's just been massive in terms of the response and

people being interested in, and thanking me. And it's

... You know, the thing is what's happening is that the

data science, it is a community. It really really is a

community, unlike anything else that I've ever seen.

Kirill Eremenko: True.

Matt Corey: I mean, I was in HR before, in change and

transformation. But data science is a real community.

They really join. They really help each other a lot.

Kirill Eremenko: Yeah. Yeah. I totally agree with that. And they share,

and they comment, give feedback like in a positive was

on what can be improved, resources like LinkedIn

articles that people are sharing and writing about their



learning pathways. Or GitHub code that people are

sharing with each other. Or comments on Tableau

public dashboards, or-

Matt Corey: Yes.

Kirill Eremenko: ... Kaggle competitions. And how data scientists

collaborate on Kaggle competitions. It's very exciting to

see people from different parts of the planet actually

come together to do these projects. So couldn't agree

with you more on that one.

Matt Corey: Yeah, it's incredible. It's a very giving community.

Kirill Eremenko: Mm-hmm (affirmative). Very excited to be part of it.

And so in terms of like ... You mentioned a couple

things. You mentioned you help people with

placements, mentoring, also rewriting CVs, or advice of

how to write them-

Matt Corey: Mm-hmm (affirmative).

Kirill Eremenko: ... LinkedIn profiles and so on. Could you give us a bit

more insights into like the different aspects that a

recruiter does? So what is the job of a recruiter in the

space of data science? Like those items that you help

people with, and maybe a bit more details on those if

you can?

Matt Corey: Well I mean, what I do is a little bit beyond what I

would say a normal recruiter does. I think that's where

... Because of my specialism in being exclusively a

data scientist recruitment practice, or a recruiter.

Kirill Eremenko: Yep.



Matt Corey: I have a sort of a greater sort of insight, a greater focus

on what I'm doing. And I also want them to succeed.

So I mean I know that personally, apart from some

people who work with me and work for me, they ...

And I try to also tell them that you need to also coach

people a lot of times, you need to help them. They need

sort of some preparation in terms of their interviews.

At times there's an issue of confidence.


Matt Corey: They need to maybe at times improve their

communication style. So it is about ... It's not just

about sending a CV or a resume, it's also about

helping this person. This person is having ... This will

have a major impact in their life, on their family, on

their whole sort of circle within either their family,

their friends, their life, their children. And it creates a

major impact. And that's why I think one of the

reasons why I'm in recruitment is because when you

help that one person get a job, you make a major

impact in their lives.


Matt Corey: And it goes way beyond just getting them a job, just

[inaudible 00:12:13] in. It's about also helping them.

I've seen so many people change not because of the

job, but because of the process of getting to the job.

Kirill Eremenko: Mm-hmm (affirmative). It's not the end destination, it's

the journey that matters, right?

Matt Corey: It is the journey. It's the process. Yeah. Definitely.



Kirill Eremenko: Fantastic. And an interesting question I had in mind

while you were speaking came to me, how often do you

see people looking for a new job because they're

unhappy in their current job? Not because simply data

science is the trendy thing to be in, but actually

because when they were choosing their original job,

they found something with a high pay, or something

that was available, something that sounded really

interesting, but they didn't do enough research to

understand is this the right thing for them? How often

does that happen that people are really unhappy in

their role, and therefore they're looking for a new

opportunity?

Matt Corey: Interesting question. I mean, I have to answer it in a

slightly different way just to sort of so I can see how-

Kirill Eremenko: Sure.

Matt Corey: ... I can best answer this. I would say that there's

passive candidates and there's active candidates.


Matt Corey: I would say that because of the way I work and others

with me, the market is primarily, we always approach

mt passive candidate as much as possible. It's not just

about the active candidates. The active, when I say the

word, "Active candidates," they are the ones out there

saying, "Here's my CV. I'm leaving in a week."

Kirill Eremenko: Oh, okay.

Matt Corey: And you have the passive ones who are in roles, so

who are either happy, going back to your original

question. Or possibly unhappy and have accepted it.



Kirill Eremenko: Oh, okay.

Matt Corey: But we always approach passive candidates as well,

because we're looking for certain people with certain

experience from certain industries.

Kirill Eremenko: Gotcha.

Matt Corey: Because our client is looking for that.

Kirill Eremenko: Yeah. Okay. Gotcha. So you kind of act as a head

hunter for the businesses, for the clients that need

those skills?

Matt Corey: Yes. I mean, it is a matter of also looking at ... You

know, we have our own database-


Matt Corey: ... of course. We have our own network. So I have my

own network that I know. I have then my LinkedIn sort

of network. I then have the database. I then also have

people who know people, who I then seek out let's just

say as an example, a data scientist who's worked in

retail and I have a client who's like saying, "I definitely

want someone from this company, X, Y, Z company in

retail." Or, "I definitely don't. I want someone

completely different. I don't want anyone from retail. I

want someone from banking or financial services. And

then who has so much experience in this specific

area."

So it's about then looking for that person. Now that

individual again what I mentioned earlier, may be

happy with where they are, they may not be that

happy. They might be happy with their salary, but

they don't like their boss, they don't like their



manager. But they're also then weighing it up and

saying to themselves, "The salary is good. My boss is

so-so." But people normally leave not because of the

money necessarily, of the salary or the package. They

usually leave because of the environment within the

company.

Kirill Eremenko: Mm-hmm (affirmative). Gotcha. No, no, I agree. Okay.

Interesting. Interesting. All right. And then so, on our

podcast, and just in our community of students, we

have quite a large portion of listeners and data

scientists. Or not actually data scientists yet, but

listeners and students who are in adjacent fields, are

in either IT, or something similar like system

administration. Something to do with technology. And

they want to move into data science. What would your

advice be for them? What is the current status of the

job market in data science? Is it a good idea to move

from IT, business intelligence, system administration

and so on into the space of data science?

Matt Corey: Absolutely. Of course. It's going to be ... I mean, I

think that every business out there in the future, if

that's in five years, if that's in 10 years, will be talking

about that they spoke to the data scientist consultant,

or their data scientist within the company. It'll be an

absolute norm in future. So do I think it's ... Yes,

absolutely. Anyone who wants to be get out of their

position, if it's from business intelligence, or from IT.

Or whoever has an interest, this passion about data

science, or to become a data scientist, do it. If they're

to do courses with you, or other providers, absolutely.

Definitely.



Kirill Eremenko: Gotcha. Okay. And so you mentioned courses. What

are the best steps to make this transition? What is

even the starting point? I get this question a lot, where

would somebody start if they want to transition into

data science? The thing is that there's a lot of demand

for data science skills, right? And some people have

already a lot of experience in something very similar to

data science. Some kind of field that they can leverage

their experience from. But at the same time, they're

not technically qualified to apply for data science jobs

that require five years experience. So somebody might

have 10 years of experience in IT, or programming, or

database design. But there's a job that requires five

years of data science experience. What would you say

is the first step? And how should people thinK about

their prior experience? Should they be like, "Okay, well

that prior experience that I have is actually now

irrelevant. And I should start from scratch." Or should

they find ways to demonstrate the values that they've

provided and actually show that it is relevant to the

role that they're seeking in data science? And how can

they do that?

Matt Corey: Well again, a very good question. It come down to the

specific role. It comes down also how long ... Not how

long. How many years they're actually looking for. So

when you have a job description, you have a role. You

have a job description and it says, "Essential." And

this is where we split things that we say, "Essential

criteria," and, "Desirable criteria."




Matt Corey: As an essential criteria, it is something that they

essentially want you to have. And at times it's flexible.

It could be, "We want experience ... " No, "Experience

in, or proven successful experience in." That makes it

quite broad. If it's, "Essential three years experience

in," and you don't have it, and you only have one year,

then you're completely then crossed out. And you'll not

be considered at all. So it is about evaluating where

you are, "Can I go for this role?" If they're asking for ...

I mean, let's put this more specific. I mean, I

mentioned the example earlier about retail. If they're

asking for a data scientist who have experience of

three years experience within a retail environment-


Matt Corey: ... of successfully implementing projects, et cetera.

Predictive analytics, et cetera. And then you don't have

it, then you can't really apply for it. Unless the field is

not ... Hasn't really ... They can't find someone to have

the three years experience. But they have someone

who has two years experience, or a year and a half.

They might then either rewrite the job description, and

allow that person to apply. That's what normally ought

to be done.


Matt Corey: But it comes down to what the employer's looking for,

and where that person is. And to what extent the

employer is willing to be flexible, and to what extent

the prospective employee is willing to either train

themselves up, go off and do a course, reapply in

future, or would be considered today because the

company's willing to train him or her to reach the level



that they are expecting. I hope [inaudible 00:20:46]

sort of answers your question. I'm not [crosstalk

00:20:48]-

Kirill Eremenko: Mm-hmm (affirmative). Yeah. That's ... I appreciate

your comment on that. I just wanted to see, what

about this scenario? For instance, the job description

says, "In retail. Data science application, prediction

and modeling in retail," and so on. "Three years of

experience." And the person applying has let's say,

three years of experience, but not in retail, not in data

science. They have three years of experience in

business intelligence and reporting in the healthcare

industry. Something kind of like technologically

relevant. But not exactly the same thing, and not even

in the same industry. But now this person instead of

completely foregoing this opportunity, and completely

giving up on it.

What they do is they go and do an online course in

data science and retail. They go to [Cagle 00:21:44]

and download datasets about retail datasets. They go

to the World Bank, or some other sources of data

science and retail, and datasets relevant to that role.

And actually do projects. They demonstrate their

capacity. So over the next six months, they do six

major projects, they write articles on LinkedIn, they

write six blog posts on LinkedIn. They share their code

on GitHub, they do [inaudible 00:22:10] dashboards

on Tableau Public. They do a Kaggle competition and

they take 17th place, and so on.

And so they demonstrate that even though they don't

have the three years of experience, they are capable of



producing the results that the employer want. What

will happen in that situation? I know it's a case by

case basis depending on the employer. But do you

think that strategy, that approach actually has a

chance with the right employers for that person to get

hired? Or is it like-

Matt Corey: Yes.

Kirill Eremenko: ... [crosstalk 00:22:40]?

Matt Corey: Personally I believe that they have ... Yes. The answer

is yes. And it would be, they have a very good chance.

It also depends on how flexible the employer or the

hiring manager is.


Matt Corey: If they are savvy enough in terms of all these

mediums, and are aware of the value of that, and are

willing to consider all these things, fantastic. But I

think a non-data science person or someone who is

not immersed enough in, not sort of involved enough,

and not aware enough, they would take it very very

strictly and just cross it out. I mean, very open about

it. But it comes down to what extent who's actually

going to be shortlisting for this role.


Matt Corey: And how strict the criteria is.

Kirill Eremenko: Mm-hmm (affirmative). Okay.

Matt Corey: So if the hiring manager tells for example HR, "I only

want this. Do not consider anything else. I don't

wanna see anything from ... I just wanna see exactly



that. I can get 500 people tomorrow who have

something which is slightly different. I don't want that.

I'm looking for exactly that." So it really comes down to

how flexible and how open-minded they are to accept

other related transferable experience.

Kirill Eremenko: Gotcha. Okay. And then flipping the coin onto this

other side, what would you say to hiring managers

who are listening to this podcast? Or to entrepreneurs,

or business owners who are looking to hire data

scientists? Should they be flexible, or should they look

specifically for that type of person from that industry

with that experience? I'll tell you my opinion on this. I

think that there's so much demand in this space of

data science, that being inflexible can be costly in

terms of time and in terms of the talent that you pass

by. But I'm really interested to hear your opinion,

because you're in this space. And you might say, "No,

look you have to. Like if you really want something

specific, you gotta stick to it and go for it." So what are

your thoughts on it?

Matt Corey: Thank you. I mean, you're obviously an entrepreneur.

And you understand that one has to be flexible, one

has to be open-minded. And I think that's a certain

mindset that not everyone has.


Matt Corey: Personally I do make recommendations. I do at times,

depending on my relationship with the client, I would

then adapt and say, "Look, I think that this person is

hitting the mark. They're not hitting the mark in

exactly the way the job description has been written.

Maybe this person doesn't have the three years



experience. However, you are looking for this and this,

and this. And in order for this project that you have at

the moment that you want someone to have ... It

doesn't necessarily require three years of experience,

because he or she has actually done this Kaggle

project, has been on GitHub, and has done very

relevant things here. If you go on their website and you

look at the projects they've done, or on Kaggle, you'll

that they've been quite high up of in terms of where

they rank. And they've done really really well. And a lot

of these comments are actually very relevant to what

you're looking for." And it's also my reputation as well

on the line, because I'm then talking to a client who

trusts me.


Matt Corey: And I also don't want to put forward a person who I

think cannot do the job. I'd rather just say, "You know

what, I'm sorry. I can't find someone." And my role is

also to send very few resumes over. I don't like to send,

if someone is a client of mind is looking for one person,

I don't send 10 CVs or resumes, I send maybe a

maximum of four.


Matt Corey: Because I want it to be the absolute best ones. And if I

send only one, then my client knows that, "Matt has

sent me the best CV, because that is the only one that

he really believes in enough."


Matt Corey: So it's at times you have to challenge in a nice way,

your client. Because you're there to also inform him or



her that, "I think in this case, you ought to see him or

her, because they have relevant experience. And I

think you'll very quickly find out. If you don't wanna

see him or her in person, and you'd like to have maybe

like a 10 minute chat with him, I'd recommend at least

that."


Matt Corey: But I would say ... I would definitely say, let's say for

example this person is another country. I mean, for

obvious reasons we're gonna have a Skype interview,

we're gonna have some sort of a chat online. Don't fly

this person in if you have some reservations. Have a

chat with him first. No, because I say that because I

have lived the experience of where we would ... When I

worked as an internal recruiter where at times maybe

we would maybe not thoroughly check it. And that's

something which I had my own views. But you have to

also work with everyone. And at times, not everyone's

perfect.

Kirill Eremenko: Yep.

Matt Corey: So it's about also ... It goes back to what we said

earlier, about having that flexibility.

Kirill Eremenko: Yeah. Yeah.

Matt Corey: And being sort of understanding what the objectives

are, and seeing if this person can actually make this

happen for you.

Kirill Eremenko: Mm-hmm (affirmative). Yeah. I agree. And so basically

that's a great transition to the ... I guess, the over your

... Of your role, a role with data science recruiter, it's



not just to find, head hunt the right people. And it's

also not just for the client. It is also not just for the

individuals who are looking for a job to put them into

jobs. Your role as I see it is much bigger than that. It's

actually being that middleman, and being that

advisor/negotiator who guides this flexibility. And it's

exactly what you said, that you need to ... Sometimes

clients, especially in this space of data science which

is so new, they're looking for something that is like a

unicorn, that doesn't exist. That person with 10 years

of experience in data science, and they could do this,

and this. When some of those technologies haven't

even been around for 10 years. And so that's where

this advice and like kind of shaping up the

expectations of the client comes in.

And I wanted to draw on my own experience in this

matter. And this is going back to when I was leaving

Deloitte, I was looking for a job. And sometimes I

would get contacted by companies directly. Like for

instance, two banks contacted me about potentially

working with them. And sometimes I would get in

touch with recruiters. And I remember this specific

day a recruiter went onto my LinkedIn, and I saw that

they look at my profile, but they didn't message me or

say anything. So I hunted them down, messaged them

myself and said, "Hey, like I noticed you saw my

profile. Is there anything I can help you with?"

And they said, "Well look, your profile looks

interesting. But the job we're recruiting for," or, "Job

I'm recruiting for is not ... It requires more work

experience." So this was a role in a pension fund that



required six years of experience. And I only had two

years of data science experience at Deloitte. And some

work experience prior to that not in the field of data

science. And in total it wasn't even close to six years.

And so as you can imagine, that's quite a large

difference. Six years in data science versus two years

in data science plus a bit of work in an unrelated field,

or not in a specifically data science field.

And nevertheless, what I told them was like, "Let's

catch up, and I'll send you my CV. Tell you about the

projects I've done. Bring you a portfolio of the projects

I've done," like a desensitized portfolio of the projects

I've done, "Just to showcase all the projects that I can

do. To showcase my abilities and show you that I can

actually deliver for this plan." And in the end after we

caught up, they really thought that I can do the job.

They recommended my CV to their client. And when I

went for the interview, I got the job.

Matt Corey: Fantastic.

Kirill Eremenko: Yeah. And that's where I worked for a year after that.

And so yeah. Just stands to show that sometimes, or

actually quite often especially with larger corporations

where this processes of recruiting are standardized,

they are still not entirely adapted to the situation in

the data science job market, and just the profession as

a whole. And so they need people like you, Matt, to

adjust their expectations, to be more flexible, and

eventually to get the candidates that might not meet

the criteria exactly, but that will get the job done, or

that actually maybe even get the job done better than

who they thought they were looking for.



Matt Corey: Yeah.

Kirill Eremenko: My question would be to you here is, how often does

that happen? How often does it happen that you help

the client be more flexible?

Matt Corey: Yeah. I think this is an excellent question for many

reasons. Because I think that is also a reflection of not

just the data science world, but this is also a reflection

... What you're entering into an area which is fantastic,

because I think it's something which isn't really

discussed enough. And I think it's something which

the industry ... Or when I talk about the industry now,

I'm gonna talk about the recruitment industry as

recruiters, I think really this is a major, major issue

that exists I think for recruiters.

Because it comes down to the recruiter being confident

enough to ... So the recruiter in your case for example

was open enough and flexible enough, and adaptable

to allow your CV, your resume, to be taken onboard.

To allow your experience. And then have the

confidence to discuss this with their client. Because

your background was not straightforward in terms of

... That recruiter had to actually to some extent

convince the client to see you.

Kirill Eremenko: Mm-hmm (affirmative). Exactly.

Matt Corey: And that comes down to ... I'll use the word,

"Backbone," or, "Confidence," or to say, "Actually, you

know what? I'm going to ask the client," and say,

"Mister or Miss client, you know, I know that your job

description says this. I know that this is what you're

looking for in terms of the essential criteria. However, I



have met someone who is meeting this, but in a

slightly different way. Doesn't meet the ... Here's

however their experience is such that I think we ought

to consider him," in your case.


Matt Corey: But that comes down to the recruiter being confident

and flexible enough, and being able to in a way, in a

nice way, challenge their client. And also then the

second party, which is the actual client to be again,

open enough and flexible, and adaptable enough to

allow another resume or CV to come forward, which is

not exactly the way the job description has been

presented.

Kirill Eremenko: Mm-hmm (affirmative). Okay, gotcha. And so how ...

Like what would you say is it? Is it 50% of your clients

that you advise that way? Or is it 80%, or is it 20%? I

just wanna get a gauge for how is the industry shaping

up? I know that a few years ago, that would've been

predominantly the case, like people getting these job

descriptions very wrong. How is it right now?

Matt Corey: I think it's changing, because we're now having a lot

more people who are ... The hiring managers are

usually data science professionals. So when I deal with

head of data science professionals who are hiring for

their team, they are aware of what the role is, because

they are essentially data scientists themselves. The

difference is that they're also head of data science, so

they are running a team. It is rare so far for me to have

people who are unrelated to data science be hiring

data scientists. So hence they know-




Matt Corey: ... the nuts and bolts of what is required. So if you

were to hire a data scientist tomorrow, you know what

you're looking for, because you also have that inside

track of knowing A, what you're looking for. And B,

you've been there before.

Kirill Eremenko: Mm-hmm (affirmative). Yeah, yeah. Gotcha. Okay. So

any ballpark estimate? How often?

Matt Corey: I'd say at the moment, the majority ... I mean, to give

you an exact percentage I would say at the moment for

me at least here in London, it's normally about 70 to

80% of people are data science professionals. And I

don't necessarily need to challenge them in that sense,

because they know what they're looking for. I can ...

There's always gonna be some flexibility I mean, if they

say three years. But it's not so much years, it's more

about having a certain experience. But I do admit that

there's a very strong industry preference. So I do have

clients who are very specific in terms of having that

industry experience. So if it's retail, they want retail.

It's quite rare you hear, "I don't want ... " If they're in

retail and they say, "I don't want anyone in retail."


Matt Corey: That's quite rare.


Matt Corey: Because there's a certain comfort. And I'm gonna say

that that is disappointing, because I have also been on

the other side of the fence as a candidate where when I

finished off with a client years ago who was in FMCG,



Fast Moving Consumer Goods. And at that time in the

market, there was a real boom about having financial

services or banking, which I didn't have. They were so,

I'm gonna use the word, "Fixated," on having that for

the extent that it's like, we were all kind of ... Anyone

who wasn't in that ... Didn't have that industry

experience, was just not invited.

So I've lived it as a candidate. I know how that feels.

And it can be very frustrating, especially when you

have so much experience that as a recruiter, I

personally have done so many different areas, that a

recruiter is a professional. And they adapt. And if you

want me to find you a sales manager, or a sales

director, or you want me to find you a fundraising

director, or you want me to find you a head of data

science, or you want me to ... There's a point where a

recruiter becomes so adept that he or she is going to

learn the industry, learn the role or the roles-


Matt Corey: ... and be ... And also know the competitors as well,

well enough. I mean, a true professional that's what

one does. You immerse yourself so much in

understanding what the role is, you even go and do ...

You spend a day, in this case for example today, with a

data scientist. You go and you ask your client, "Can I

sit in within a meeting and understand things, how

they work here?"


Matt Corey: So it's about immersing yourself. And yeah, it is about

... But to go back to your original sort of question, it



really comes down to the person. The majority of them

in my case are data science professionals.

Kirill Eremenko: Mm-hmm (affirmative). Yeah. Gotcha. And what you

mentioned about the industry focus, I agree with you.

It is disappointing, because in addition to your points,

it is such a flexible profession. Knowing how to deal

with data in the health industry, and then taking that

skill and learning how to deal with data in the

entertainment industry, or in the public services

industry, it takes a couple weeks maximum for

somebody to gain all that domain knowledge, the core

domain knowledge. Of course there's gonna be details

that you will learn along the way. But the working of

the data part of the skill is extremely transferable. And

I know that coming from consulting where at Deloitte,

one day I was working on a railway. Another day I was

working like analytics for a railway company. Another

day I was working on a healthcare industry. Another

one I was working for a mining services company.

So very very transferable skills. If I was recruiting for a

data scientist right now, and I was in a specific

industry, the last thing I would put on my job

description is, "Industry specific experience." Because

ultimately that is not relevant at all. What are your

thoughts? Do you agree with me on that, or do you

have a different opinion?

Matt Corey: I agree, and I'm gonna say both. I'm sort of on the

fence with it, because I'll tell you why.

Kirill Eremenko: Okay.



Matt Corey: It comes down to as an individual, I'm absolutely

100% behind you. Because I want to give everyone a

chance.


Matt Corey: I think it comes down to also how pressing it is,

because if the industry is quite complex. And if for

example there's a project that involves someone to

know the expression, "Hit the ground running," and

really be able to very very quickly be knowledgeable

enough to such an extent that they would have to

really know the industry well, because the project is

for three months, the project is for six months max.

And it really requires someone to have a certain

amount of industry experience. That is where I would

say I understand it.

Kirill Eremenko: Yeah. Yeah. Gotcha.

Matt Corey: If it was a permanent role, I would say no. I don't think

it requires in this case. And also depending on the role

in general. But I think the more ... The less time you

have, I think it's justifiable to say, it is all right ...

Again, depending on how important the role is with

respect to having some industry experience.

Kirill Eremenko: Mm-hmm (affirmative). Yeah. Okay. Makes total sense.

And can you tell us a bit how often do you recruit for

permanent roles, versus temporary roles like you just

mentioned, six, 12 month projects? What is kind of the

slit that companies are looking for?

Matt Corey: It's primarily in my case here in London, it's primarily

... Or in the U.K. I would say it's more so on the

permanent side than the temporary. I've also worked



more in the permanent market. But I would say so far

for me, it's been more on the perm side.

Kirill Eremenko: Mm-hmm (affirmative). Why would you say that is? Is

that because companies wanna build out their internal

data science divisions more than they just wanna get a

project done?

Matt Corey: I think it's also there's a cost element as well. Because

when you hire someone on a permanent basis, it is

more cost effective as well. When you hire someone, in

this country at least, on a temporary basis, you're

hiring them as a contractor. You're paying them more,

much more than you would be paying them on a

permanent contract. Or at least in this country again,

we also have a term called fixed term contract, which

is for a year or two years.

Kirill Eremenko: Mm-hmm (affirmative). Gotcha.

Matt Corey: Which can be ... So if I gave you a salary in terms of

U.K. Pounds. So if I said to you that someone's earning

£70 000, U.K. Sterling, versus someone who's earning

then ... What can I say? A salary from 70 000, then

they would be earning something like ... I don't know if

they were earning 600, 700 a day, 800 a day, 900, a

1000 for example, a day. That is a very very different

sort of model in terms of hiring someone on that basis.

And it's quite costly. And in this case also, in this

country at the moment, the public sector which is

government, doesn't normally hire at that rate as

much. It's been ... Things have changed here. So it

comes down to also, are we talking about the private

sector or the public sector? So we know there's private

sector of course, private companies. Or public sector



meaning government. And obviously if we look at this

as a global podcast in every country, it's different.


Matt Corey: If it's the U.S. If it's in India, if it's Australia, if it's the

U.K. If it's Germany. It's different every single market. I

mean, now we're talking about sorta local differences.

Kirill Eremenko: Yeah, okay. Fair enough. Gotcha. Okay. That was

quite insightful into the world of recruiting. Thank you

for that little discussion. And now I wanted to move on

to something a bit different. And that is your book.

Congratulations, your book just got published. It's

very exciting to see it on Amazon. And-


Kirill Eremenko: ... you showed me the hard copy when we were talking

on video. So how are you feeling about that? Must've

been quite a lot of work that went into it.

Matt Corey: Yes. I mean, it was quite a bit of work. Surprisingly I

wrote it I think within a few months. And I think it's

been an amazing, amazing learning curve in terms of

writing a book. I think people say, "Oh, wow. You

wrote a book." Or you know, "That must be amazing. I

would've never thought of writing a book." And I had

thought about writing books, but not necessarily ... I

never thought I'd write a book so quickly.


Matt Corey: And I never ... I think in your book, Cognitive Data

Skills, as you mentioned one doesn't sort of grow up

and think that they wanna become a data science

necessarily when they're growing up. But I never



thought growing up that I'm going to be writing a data

science book of quotes.

Kirill Eremenko: Yeah. Oh, yeah. I'm sorry. For the listeners, I forgot to

mention the name of the book is Data Scientist's Book

of Quotes. Please continue, Matt.

Matt Corey: Thank you. Thanks so much. Yes, the book is

available at the moment on Amazon as a Kindle book.

And the paperback will be available hopefully in about

from let's just say on the safe side, will be about

maybe 10 days to two weeks.

Kirill Eremenko: Yeah. Well, by the time this goes live, it'll probably be

available. We have-

Matt Corey: Okay.

Kirill Eremenko: It'll live in a few weeks anyway. And I wanted to say

that I had a look at some of the quotes. I don't have it

yet, but I'm definitely gonna order it as soon the hard

copy's there. And I had a look at some of the quotes

examples on Amazon. You can do a quick preview of a

book, it'll show you a few pages. And so basically it's

broken down into different chapters where you can ...

You get quotes from different people in that space. For

instance here's one I like, "Without a grounding in

statistics, a data scientist is a data lab assistant."

That's Martin Jones, Managing Director in Cambrian

Energy.

Here's another one, "Data scientist are kind of like the

new Renaissance folk, because data science is

inherently multi-disciplinary." John Foreman, Vice-

President and Product Management of Mail Chimp. So

some very interesting ones that make you pause and



think. And it reminds me of the book I'm reading now.

What is it called? The Art of Life. It's about stoic

philosophy, but explained in simple terms. And it's got

a lot of these, not quotes, but kind of like little

passages. And there's no way you can just sit down

and read cover to cover in one day. Because even

though it's a small book, simply because it provokes so

much thinking. And what-

Matt Corey: Yes.

Kirill Eremenko: ... I like about like a book like yours, like with quotes,

whereas you open up a page and you read a couple of

quotes, and then you sit down and you think about

them. And it provokes some new ideas in you. And on

top of that, what I found useful, or I'm looking forward

to finding useful when I read your book is that you

broke it down into chapters by grouping the quotes

together by their different style ... Or not style. More

topic.

So for chapter one is like, "What is a data scientist?"

Chapter two, "Power and potential of data and data

science. Data's value." Then you go all the way onto ...

Let's go through them, "Treatment of data." Chapter

four, "Not having the right data. Potential risks of data.

Challenges with data. Machine learning. Deep

learning. Artificial intelligence. Data ethics. And data

privacy. Future of data." So if I'm gonna be like, "I

want to learn about ... "

I wonder, "I have a problem on data ethics," that I have

a discussion with someone I need to have soon, I'm

gonna open up chapter 11. And I'll read a couple of

quotes on data ethics and privacy. And [inaudible



00:47:57]. And I again, I haven't read it. But it sounds

like a book good to have, nice to have in your library

for the time when you're gonna need to pull out when

you have some free time, or you need to learn a bit

about it. So really cool idea. How did you come up with

the idea for the book?

Matt Corey: Well, I thought I definitely want to be ... I want to write

a book. And I thought, I'm not at a point where I'm

that knowledgeable yet to write an entire book. I mean,

I'm fascinated with how to create a data driven

organization, how to have a data driven culture. I'm

fascinated of course with the role of the data scientist.

But I thought, "Do I have enough knowledge yet to

write a book today?" I mean, I don't mean within a few

months. And then suddenly I just, I saw some other

books on the market, different subjects. And I thought,

"Wow, you know what? I can actually write."

And I checked it up and thought, "Well, I didn't see

any book like that out there." And I thought, "You

know what? I can actually write a book of quotes,"

because I know there's obviously books from

literature, et cetera where they have quotes from

people. Sorry. And I also think that because I'm also, I

literally write quotes in, I have these books, these

journals. So I think we talked a little bit about before

where I'm a huge Tony Robbins fan.


Matt Corey: And I have a few books of his with quotes. I have a

journal of quotes by him. And he also quotes people in

the past when he first started his career. And he

literally has a book of quotes from people that he



admires. And I remember writing a lot of these quotes

in my own journal. So I have a kind of predisposition

to writing quotes. Because I think that this is where

people provide these nuggets of knowledge and also life

experiences. And it makes you really wonder.

Because I am a believer that life is very short. And life

can be very full. And I do view life as being half full

and half empty. And it's what you make of it. And it

really is about making the most of it and doing your

absolute best every day. And how you think and what

you believe in, and if you believe the worst, then the

worst will happen. If you believe in the best, the best

will happen. You stumble along the way in life, but you

need to pick yourself up, dust yourself off and keep

going. And I'm a firm believer of that. And there is a

book that I read many, many, many years ago. And it

absolutely changed my whole life.

And that book today, I mean it still is out there. And

it's called, The Power of Positive Thinking by Doctor

Norman Vincent Peele. So that was the book that for

me I'm gonna say ... Oprah says that books are her

friends. That book not only was my friend when I was

17, but it also in a way saved my life in a sense,

because I didn't do well in school on a certain course.

And I remember literally failing that course. Here I am

publicly saying that. And what happened was that I

read that book during that Summer. And I enrolled in

that course again. And I went from a failing mark to

passing it with 89.

Kirill Eremenko: Mm-hmm (affirmative). Wow.



Matt Corey: And did I become a genius in that course? No. I simply

believed enough, and I studied enough, and gave my

all to pass it. And I came in second in the class. And I

was able to continue my education as a result of it.

Because I wouldn't have been able to go to university if

I didn't.

Kirill Eremenko: Mm-hmm (affirmative). Gotcha. Okay. Wow, okay.

That's a little interesting that you got the idea for this

book. But yeah, I think it's gonna be a great success,

and great help to many people in field. I guess we're

talking about data science being a community. And I

think we needed some kind of resource like this to be

able to reference different people. My question too

[inaudible 00:52:34], what's your favorite from your

book? I think you have like 320 quotes in there or

something. What's-

Matt Corey: Yeah, that's right.

Kirill Eremenko: What's your favorite one?

Matt Corey: Wow. Oh, that's a question.

Kirill Eremenko: Weren't ready for that, were you?

Matt Corey: No, I wasn't. I wasn't. I'm just thinking, "Oh, what do

you say here?" You know what? The thing is, I also

have many quotes which in the book, I know that you

maybe can't see it at the moment because of the fact

that you have the sample.

Kirill Eremenko: Yeah, yeah.

Matt Corey: But I will ... There's a lot of people in there who, like

Warren Buffet, and Tony Robbins, and Bill ... Okay,

Bill Gates, that I have quotes from. And something else



which you, just to mention that after each chapter

there are exercises with questions.

Kirill Eremenko: Oh, wow.

Matt Corey: And there's some notes. So people can actually answer

the questions for themselves and for their

organization.


Matt Corey: So I'm gonna say one quote which really does stand

out of me. But it's not necessarily data science quote.

So is it okay if I mention this one?

Kirill Eremenko: Yeah, yeah. Yeah, of course.

Matt Corey: Okay. "Your work is going to fill a large part of your

life. And the only way to be truly satisfied is to do what

you believe is great work. And the only way to do great

work is to love what you do. If you haven't found it yet,

keep looking. Don't settle. As with all matters of the

heart, you'll know when you find it." That's by Steve

Jobs.

Kirill Eremenko: Wow. That's a really cool quote. And very also right in

time for this podcast, right? 'Cause we were talking

about recruiting and head hunting, and how to find a

job. Really cool. I really appreciate you sharing that.

Great. Hopefully that will-


Kirill Eremenko: ... get people thinking, is your heart in what you're

doing? Or is it not? Matt, is your heart in what you're

doing? You've been doing it for a year. How are you

feeling?



Matt Corey: I love it. It's what I mentioned to you earlier that I'm

excited by it for many reasons. And thank you for your

question, because I'm excited with the fact that it's

fresh. It's really in demand. It's much needed. You

really can work in a much more efficient manner. And

when I talk about sustainability, this is what I'm

talking about really. When I heard a statistic a while

back that we only use ... And I think, I'm gonna say we

only use about ... And this is even the max. And I

think it's actually 1%. But I'm gonna say 5%. I'm

gonna be even more ... I'm gonna raise it up a bit more

and say that a company or an organization only uses

5% of its entire data.

Kirill Eremenko: Wow.

Matt Corey: That is shocking.


Matt Corey: Shocking that they don't fully utilize their data. And a

data science and others in the data science arena can

fully utilize all of the data. And I think that's where, to

be on that sort of cutting-edge of a profession that is

so much needed. And I'm gonna say something else

that has to do with life. It has to do with companies

who are out there, small and medium companies that

don't have a lot of resources, that don't have a lot of

time and money. But they're able then to fully utilize

their data. It saves them time. It saves them money. It

saves them hardship. It saves them ... I can tell you

from my own personal experience. And if I had known

that I can maximize my data in my own past, I would

say that I would be in a different place today.



But I say that in a very honest and very open manner

that by utilizing a data scientist for one's own

business, either as a consultant or as an employee,

you are working in such a more efficient and effective

manner. So yeah, I am very passionate about it. And it

is something which I do love. And I also love the fact

that this community is such that it's a very open, very

giving, very new, very helpful, and you used the word,

"Sharing."


Matt Corey: I think it's something which the community itself is

very helpful, very giving, and willing to help each

other. Very very much so. And in terms of resources.

And LinkedIn is a primary example. I mean, we

wouldn't have been talking today if it wasn't for

LinkedIn. And LinkedIn is, you see so many books

being offered. So many resources being offered.

Algorithms, et cetera, "Use this." And, "I'm learning

this. And this is how I got my job. And this is how I ...

This is what I did." And there's a lot of sharing.

Kirill Eremenko: Mm-hmm (affirmative). Yeah. Wonderful. Thank you so

much Matt, for those insights. I totally, totally

appreciate your comments. And it's exciting to be a

part of this community, exciting to be a part of this

broader group of people who are all passionate about

the same one thing, which is data science. So thank

you so much. I think we'll wrap up the podcast on

that. I really appreciate you-

Matt Corey: Okay.

Kirill Eremenko: ... coming on the show today sharing your insights.



Matt Corey: Thank you for inviting me.

Kirill Eremenko: Where would you say is the best place for our listeners

to find you, contact you, get in touch, or follow your

career? Or maybe some people are looking for jobs and

would like to get in touch to a recruiter. There might

be companies that are looking for a recruiter to help

them out. Where is the best place to do that?

Matt Corey: Well, the website is ... So for the business, the

Recruitment Change Force is the business. And as I

mentioned, it is an exclusively data scientist

recruitment practice. So that's on changeforceinc.com.

So go Change Force INC.com. And my details are

there. So in terms of phone number and the company

sort of details. I'm on LinkedIn. So it's Matt Corey. It's

M-A-T-T and then C-O-R-E-Y. So I'm on LinkedIn if

someone wants to ask me a question. So there's that,

and the business details will be on the website. The

book as yeah mentioned, [inaudible 00:59:28] thank

you for that again, is on Amazon. I think that's pretty

much it. I mean, I'm the kind of person who either

myself or my staff are very, we do our best to help

people, and to find them roles, relevant roles for them.

And yes, it is about data science, but we're always

open to hear, to help people in general.

Kirill Eremenko: Gotcha, gotcha. And just-

Matt Corey: [crosstalk 00:59:53] data science professionals. Yeah.

Kirill Eremenko: Yeah. Just to reiterate, the book's called Data

Scientist's Book of Quotes. All right. Well, we'll have all

those links on the show notes for this episode. And-




Kirill Eremenko: ... on that note, thank you very much again, Matt, for

coming on the show and sharing all your wonderful

insights and knowledge with us, with the listeners of

the podcast.

Matt Corey: Thank you, Kirill. I appreciate. Thank you so much for

your invitation again.

Kirill Eremenko: So there you have it. That was Matt Corey, a data

science recruiter, and author. I hope you enjoyed

today's episode. And I hope you will pick up a copy of

Matt's book, the Data Scientist's Book of Quotes. As I

mentioned on the podcast, I think it's a very necessary

tool for people, especially data scientists to have to just

take time to ponder philosophically about our industry

and about the work that we're doing, and maybe come

up with some new ideas based off or inspired by other

people's quotes. People who are leading this space.

And I'm curious to find out what your favorite part of

the episode was. My favorite part was probably when

we talked about the intricate role of data science

recruiter, a good recruiter. Not somebody who just

tries to match the job description and find the right

people who exactly match the specifics, but somebody

who can talk to the clients about managing their

expectations and maybe adapting them to who's

available in the market, and what kind of skills are

there. And understanding their actual needs, because

sometimes companies create these job descriptions,

and they ... Even though they describe what they think

they want, it's not actually what they want.

And on the other hand, a good recruiter should also

work with the candidates to help bring out the true



nature of their experience. The true value that they

can bring to the company, and help them see more

about themselves than they actually think. So see

those hidden maybe gems in their experience and their

expertise, and their background that might be valuable

to different job roles in different companies.

So all in all, it was fun episode. And I hope you learned

a lot. You can and probably you should connect with

Matt, because it's always good to have a recruiter in

your network on LinkedIn. We'll include Matt's URL in

the show notes, which you can find at

www.superdatascience.com/179. There you'll also find

all of the links to the materials we mentioned in this

episode, plus the transcript for today's show. And on

that note, I hope you enjoyed the episode. Can't wait to

see you back here next time. And until then, happy

analyzing.


sds podcast episode 179 with matt corey€¦ · kirill eremenko: this is episode number 179 with...

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