sds podcast episode 179 with matt corey€¦ · kirill eremenko: this is episode number 179 with...
TRANSCRIPT
Show Notes: http://www.superdatascience.com/179 1
SDS PODCAST
EPISODE 179
WITH
MATT COREY
Show Notes: http://www.superdatascience.com/179 2
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
Show Notes: http://www.superdatascience.com/179 3
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.
Show Notes: http://www.superdatascience.com/179 4
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
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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
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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.
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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
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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.
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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.
Kirill Eremenko: Mm-hmm (affirmative).
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.
Kirill Eremenko: Mm-hmm (affirmative).
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.
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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.
Kirill Eremenko: Mm-hmm (affirmative).
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.
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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-
Kirill Eremenko: Yeah.
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
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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.
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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."
Kirill Eremenko: Mm-hmm (affirmative).
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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-
Kirill Eremenko: Yeah.
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.
Kirill Eremenko: Mm-hmm (affirmative).
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
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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
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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.
Kirill Eremenko: Mm-hmm (affirmative).
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.
Kirill Eremenko: Yeah.
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
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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.
Kirill Eremenko: Yeah.
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
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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.
Kirill Eremenko: Yeah.
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.
Kirill Eremenko: Mm-hmm (affirmative).
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."
Kirill Eremenko: Yeah.
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
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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."
Kirill Eremenko: Mm-hmm (affirmative).
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
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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
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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.
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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
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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.
Kirill Eremenko: Mm-hmm (affirmative).
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-
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Kirill Eremenko: Yeah.
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."
Kirill Eremenko: Yeah.
Matt Corey: That's quite rare.
Kirill Eremenko: Yeah.
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,
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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-
Kirill Eremenko: Yeah.
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?"
Kirill Eremenko: Yeah.
Matt Corey: So it's about immersing yourself. And yeah, it is about
... But to go back to your original sort of question, it
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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.
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Matt Corey: It comes down to as an individual, I'm absolutely
100% behind you. Because I want to give everyone a
chance.
Kirill Eremenko: Yeah.
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
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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
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meaning government. And obviously if we look at this
as a global podcast in every country, it's different.
Kirill Eremenko: Yeah.
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-
Matt Corey: Thank you.
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.
Kirill Eremenko: Yeah.
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
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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
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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
Show Notes: http://www.superdatascience.com/179 32
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.
Kirill Eremenko: Yeah.
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
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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.
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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
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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.
Kirill Eremenko: Mm-hmm (affirmative).
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-
Matt Corey: Thank you.
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?
Show Notes: http://www.superdatascience.com/179 36
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.
Kirill Eremenko: Mm-hmm (affirmative).
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.
Show Notes: http://www.superdatascience.com/179 37
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."
Kirill Eremenko: Mm-hmm (affirmative).
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.
Show Notes: http://www.superdatascience.com/179 38
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-
Matt Corey: Thank you.
Show Notes: http://www.superdatascience.com/179 39
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
Show Notes: http://www.superdatascience.com/179 40
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.