JESSICA LI: Hi, everyone, I'm
here with James Martin,
responsible for data science and
product analytics at Google.
After graduating college and
moving to a new city, I didn't
know anyone and I was on the job
hunt.
I know that experience is not
unique, it can feel overwhelming
to try to get your foot in the
door without a personal network
to connect you to the right
people or hiring manager.
Breaking into a new industry is
one of the hardest and most
common challenges that people
face today.
James is here to share how to
get your first data science
interview with no personal
connections.
JAMES MARTIN: Good morning.
Today, we're going to talk about
the tips and best practices
around getting your first
interview in data science.
We're going to cover some of the
challenges facing job seekers,
some of the tips that you can
practice and follow to get
yourself noticed, and a few
generally on setting yourself up
for success throughout the
interview process.
I've been lucky to work in the
field for the last seven years
or so.
And I've seen the differences in
the function across industries
and companies all over the
world.
Obviously, the industry's been
evolving rapidly.
The demand for hiring has been
growing exponentially.
I see that as a prudent place to
kind of start this discussion.
We're approaching this from the
perspective of, how do I get my
first interview?
But in reality, it's how do I
successfully pass my first
interview?
So to achieve either of those
objectives, it's important to
first take a look at what the
job market tells us and how we
can best prepare for success.
So the world of data science.
So data science is arguably one
of the fastest growing job
markets.
And there's almost a tangible
hype around the name.
And this creates a challenging
situation as you'll find if you
research data science job
postings that in this day in
age, almost anything and
everything in analytics can be
dubbed data science.
I'm not here to assess the
validity of some roles versus
the others.
But there may be roles titled
data scientist that are, in
fact, not.
So when we're applying to jobs
with no connections, there is
some responsibility for
identifying which roles are
actually data scientists and
which are titled as candidate
attraction.
In reality, the data scientists
can do almost anything given
however broad the field is, the
application of analytic
techniques, making business
recommendations, creating
machine learning models.
These are all form part of what
a traditional data scientist may
do.
The variety and the extent to
which each of these areas is
focused on can determine a lot
about the skill set required and
the expectations of the role.
For me, throughout the last
seven years working across this,
I've tried to think of the
various roles within the data
scientist as being on a
spectrum.
With some functions or positions
being more program oriented and
others being more geared towards
kind of depth of analytics.
Maybe user facing backend while
others may be more closely
aligned to product development.
In this example, which is only
an example, I could have put
hundreds of titles on here.
But you have Machine Learning
engineers that might focus on
coding, building and
implementing algorithms and sort
of the traditional data
scientist roles that focus
heavily on statistics and kind
of driving model through the
statistic rigor.
And then, data analysts, which
are taking on more and more of
the data science titles with
functions planning, regression,
dashboarding, data pipelining,
again, sometimes being business,
consumer or backend facing.
Now, again, this isn't
questioning the validity of one
role versus the other, but
simply highlighting all of the
functions may have some overlap,
even share responsibilities and
that more and more are simply
being called data scientists in
job postings.
So understanding this is really
important for your career road
map, in terms of the direction
that you're headed.
And given it's very typical to
see these all being called data
scientists, we have to
understand in reality, they all
play a part in driving business
decisions through the use of
data.
But clearly, we can see just
between these kind of four
examples here that there are
going to be a lot of potential
limitations on applicants based
on the function of the role and
not the title.
So the reality that we face is
that while they may all be
called data scientists in
external postings, the
functional data of the work can
be completely unique.
Machine Learning engineer or
data scientist may be highly
skilled in unsupervised deep
learning with heavy
implementation on models, a data
analyst may be an expert in
dashboarding and regression.
To be fair, one piece often
overlooked, not everyone wants
the coding heavy roles, but
that's a very important aspect
for others.
So with this context, and why I
wanted to start here first is
guessing your first interview in
data science might not be as
straightforward as you think.
It may be impossible to display
strengths in all of the areas
that would satisfy every
possible iteration of data
science from Machine Learning,
Programming, Statistics, various
types of analytics.
How do we set ourselves up for
success in one of the most
competitive job markets
globally?
So the first place that I
wanted to talk about here is
research.
Now that we know we have to make
a concerted effort to ensure the
jobs we're applying for are
actually aligned with our skill
sets, our interests and our
goals.
This is where research plays an
important aspect in the job
search.
It isn't enough to mass apply to
any and every data science title
with a goal of standing out,
getting noticed, we need to be
sure we're displaying related
skill sets that kind of line
with the expectations of the
role itself to demonstrate
suitability to whomever is
reviewing the applications.
The tip here is to invest time
in researching different job
descriptions.
Specifically, to determine the
nuances of the role for a
specific company or industry
with a clearer understanding of
the differing responsibilities,
the expectations, the
stakeholders, the impact of the
role.
You're much better equipped to
explain why you're a fit and the
value that you can bring.
Thus, immediately giving you a
better chance of getting
noticed.
And landing your job offer.
In addition to that, and this is
kind of my favorite part about
it, it actually gives you some
tangible evidence to support
your own personal decision as to
whether or not the role matches
your expectations and career
path.
Although, it is only one data
point and we know that's not
enough to form a solid
hypothesis.
It can help to sort of piece
together whether or not you feel
wholistically an opportunity is
for you.
In this regard, it's fair to say
that job descriptions are an
invaluable resource, however, we
do need to be aware of the fact
it's just one piece of the
puzzle.
If job descriptions are
potentially biased or contain
incomplete information, the
question then becomes how can we
more accurately assess if a role
is actually aligned with what we
want to do as a career or at the
very least that it matches what
we want and we're a fit for the
opportunity.
So this is where detective
skills come in hand.
Using GitHub, linkedin,
searching for open source
projects, that can help you find
out more about what the data
science teams and companies
you're interested in are doing.
And at the very least, will give
you insight to whatever publicly
available information there is,
the skills and the experiences
of existing team members.
So assuming we've followed the
steps so far, we're aware that
not every data scientist role is
the same and some may, in fact,
not be aligned with your
interests.
We're able to identify where we
can take our career.
And that's super important
because we're now in a position
where we understand the roles
we're looking for.
And with the information we have
of your experience, either
academic or industry, or
self-taught, we can begin
crafting your resume to
demonstrate the skills and the
experiences that you have gained
that are in line with the
expectations of the role rather
than the title.
I find this is really important
step in any application process.
I think, specifically, with data
science, particularly, because
for every opportunity you're
applying for, there's thousands
of applicants coming in.
So this is a really clear path
to standing out and getting
yourself noticed.
Paying particular attention to
the themes of your experiences
in education within whichever
core area is, whether it's
Machine Learning or programming
or statistics or broadly
speaking, analytics, whatever
aligns best with the opportunity
you're interested in.
An example of this done poorly
would be applying to a highly
statistical data scientist role
with little or no information
supporting the experience you
have in applied statistics,
other academic or industry.
Conversely, if you have
experience in those fields and
you're presenting information on
what you've learned, how you've
applied it, you're going to have
a stronger chance of getting
recognized.
One of the hidden benefits and
kind of why I wanted to bring
this piece up is this creates an
opportunity for you to think
wholistically about your
experience and become more adept
articulating your strengths from
different perspectives.
This is directly relevant for
some of the things we'll discuss
later, which is how do you
approach the interview itself?
An example here would be, if
you're interviewing with a
nontechnical person and talking
purely in the technical terms,
you're probably not going to
catch their attention as much if
you can present your experience
anecdotally.
I agree, yes, it does take more
time and more investment, but
operating in a targeted way
should yield better results.
So this gives us a better
understanding of the job market,
how we're selecting the job
openings, and how we're
presenting our experiences to
maximize the opportunity to
secure an interview.
What else?
This is something that is kind
of very near and dear to me and
something that I admittedly was
never very good at, at the
start.
And it ties into the second part
of the discussion which is
highlighting, specifically, we
don't have industry connections
that help us throughout the job
search.
I want to preface that it's not
a prerequisite to getting a job.
Yes, it can help.
But many of the hires that I've
seen over the last seven, eight
years haven't had any
established networks in the
field.
Whether through a career switch
or through, you know, just
having graduated and not having
networks outside of your
college.
And even in some instances, it
isn't always helpful.
Those companies might not be
hiring or the connections might
not be the right people to make
a decision.
But networking is always a
beneficial way to understand how
the industry's developing, what
skills are in demand, what
companies are working on, and
that gives us direction either
for self-betterment or to help
us target more where we're
applying for jobs in areas of
high growth.
If you consider I mentioned
before, the world of data
science is vast and ever
expanding.
But if you look at the, you
know, the example of a spectrum,
like, we can divide that big
world into much smaller
specialized communities with
which we can engage.
And these tend to be much more
accessible and open through
professional networking, open
source projects or industry
events.
It's a good way to consider how
I'm actually trying to network
either through connections on
Linkedin or similar.
Having a strong professional
network preference on Linkedin,
GitHub or similar, following key
industry figures or blogs, it is
a great resource to see what
industry professionals are
talking about.
And it gives you a way into that
conversation.
Attending events is a great way
to give you a platform in which
to build genuine connections and
real networks.
As I mentioned at the start of
this slide, if you're like me
and you aren't the naturally
outgoing gregarious type, the
events are still very valuable
to learn from the speakers.
And this gives us an opportunity
to follow up during or after the
event on email, Linkedin or
similar with a targeted question
about their topic, presentation
or their experience.
And it's surprising how
responsive I find people in this
regard: The other benefit, there
are literally hundreds if not
thousands of data science events
across the US and all over the
world where industry leaders are
talking, networking happens, new
techniques are discussed and
typically, some sort of
recruiting will take place.
So with no connections, this can
be a really good way to kind of
kick start your career or your
network, sorry, with potential
advocates.
A different approach to
networking would be to rely on
your alma mater.
Connecting with groups from
college, with professors,
background of connections in
industry.
Just for guidance, really, more
than anything is a really good
way to kind of start things,
too.
On top of that, most companies
have some sort of affiliation
with colleges, either for
recruiting or work.
That should help you kind of
build your network.
My preference in conversations
like this is not to kind of stop
short of, like, cool, how do I
search?
And how do I apply?
When we've gotten to the point
where people are responding to
us or we have initial phone
screen set-up, what do we do
next?
I wanted to focus briefly on
what I think is a very important
part and intrinsically linked to
this is how do we make sure
we're setting ourselves up for
success when we actually have an
interview scheduled?
Let's assume you've followed
all of the steps we've kind of
talked about, you've
successfully got your first
interview, what can you do to
make sure you're giving yourself
the best chance to shine?
As a general rule, I've done
this forever, I advise a
three-step process to preparing
for any interview.
Whether it's your first or your
101st.
Begin with knowing your resume
inside out.
This is the first piece of
information that any interviewer
or screener will have.
And that is likely where your
first couple of questions are
going to come from either as
icebreakers or let's get to know
each other.
Ensure you're investing time in
your job industry experience or
academia that can help
demonstrate the strengths in the
technical and nontechnical
aspects of the role.
The second and I actually find
this really fun.
We know that going into an
interview, there's going to be a
mix of theoretical and practical
questions in your core area
whether it's statistics or data
intuition or machine learning or
programming.
Focus on the themes of, what
have you done?
How did you do it?
And how could you have done it
differently?
This to me is probably
relatively unique for how people
prepare for interviews.
But it gives you the opportunity
to prepare yourself for
questions coming from different
angles.
It's really easy whenever we're
sitting down and thinking,
right, I've done X, Y and Z and
I'm going to get a question on
it, this is the question I think
I'm going to get.
You're doing it regardless of
best intentions.
You're doing it from your
perspective of, what do I think
they're going to ask me?
If you consider other
approaches, other methodologies,
it gives you the opportunity to
demonstrate the depth of your
core area but also the breadth
of your skill set through an
appreciation for different
approaches and solutions.
And on top of that, it helps you
kind of frame everything with
this critical rationale or
critical thinking while you're
communicating through
uncertainty.
The next piece here is trying to
enjoy the process.
Now, I realize this is probably
a controversial tip.
But I've always found that
people are at their best when
they feel relaxed and they're
comfortable with the kind of
flow of questions.
And the easiest way to do this.
The interview at the end of the
day, it's your time to shine,
it's your time to talk about
everything you've done in your
career to get you to this point.
And interviewers are not
probing, necessarily, to trip
you up.
They only know you from your
resume.
And that's never going to be
enough to capture who you are as
a person or the value you can
bring beyond just some words on
the page.
So the best way to practice is
practice.
So talking about your work,
talking about your ideas, your
areas for development, how you
can address them.
Think about how you would
actually assess the work or the
experiences that you've had.
What could you have done
differently?
An example would be, like, has
this, would it yield a different
result?
Would the results be the same
but the narrative was different?
What if the parameters of the
project changed?
If the variables were
different?
What would you have done?
This is for me, super
important.
One thing we have to recognize.
If there are so many iterations
of data science, that we have to
also assume that one company and
another company will have a
different definition of what
that role will do.
So therefore, generating fresh
perspectives, being open to
trying new things, it's going to
help avoid pigeon holing your
experience to one bucket.
Now, I appreciate all that might
sound like an exercise of
futility as almost anything can
be different at any point.
But practicing this forces you
to think about scenarios from a
different perspective.
And that grows your ability to
handle difficult questions
because you've already started
thinking in that way.
And then, you can focus on
enjoying the conversation or
trying to enjoy the
conversation.
And really shifting the
perspective of the interview to
assessing whether you want to
join instead of solely hoping to
impress the interviewer.
So in summary, remember that the
data science job market is
constantly evolving.
The demand for data scientists
is continuing to expand.
And with that, the functions
that are called data scientists
are also growing and evolving.
Being aware of this, being aware
of your skill sets,
understanding the industry
further in terms of the type of
role you're looking for, the
direction you want to take your
career, how you can best present
your resume, that will all help
you stand out in a highly
competitive field.
Taking an active approach.
Now, I emphasize the word
"active" approach to all of
this.
Whether that's networking,
following up on posts or blogs
with targeted questions,
attending events looking for
open source projects.
Really just trying to create
visibility for yourself on
professional networking sites.
This helps you grow your pool of
advocates or associates and your
presence in the industry that at
some point will give you
information that can help you
within your search.
The next piece, creating
tailored resumes.
Preparing thoughtfully to assess
your experiences is a great way
to instill confidence in
yourself that you can articulate
the value you can bring to any
organization.
And finally, the job market
moves quickly.
Right?
So practicing interviewing or
discussing will pay dividends in
creating that consistent theme
of I know my elevator pitch, I
know how to present my work, I
know how to talk about the value
I can bring.
Interviews shouldn't be a
dreaded exercise that kind of
create anxiety.
You know, being nervous is fine,
right?
It's very natural.
But just remember that this is
your opportunity to shine.
It's your opportunity to talk
about yourself.
Committing to and trusting the
process, I know that's very
cliched and cheesy.
But it's okay to be
disheartened, right?
If one or 100 opportunities
don't work out.
If something doesn't work out,
it just means not right now, not
forever.
Learning from your experiences
and applying those to improve
your chances over time is always
going to be the best course of
action.
Thank you for listening today.
I hope this was useful, I'm
happy to connect on Linkedin.
I'll be available to answer any
questions.
And lastly, if anyone is
interested in opportunities with
Google, please visit the career
site for data science.
We just had a revamp.
So hopefully, it's easier to
navigate.
My team reviews all of the
applicants.
So, yeah.
Thank you all so much.
JESSICA LI: Hi, James.
JAMES MARTIN: Hi.
JESSICA LI: Thanks for an
awesome presentation on job tips
and interviewing.
Let's move into the Q & A
portion of the talk now.
We have a couple of questions
from our community.
The first question is from
Purva.
Her question is, what is the
tradeoff between developing
projects based on key words
which are usually in the job
description versus developing
projects based on your own
passion?
JAMES MARTIN: That's a really
good question.
I think the advice here has to
be, like, you want to follow
your career in an area that you
actually want to do, right?
You know, just trying to tailor
your resume in the sense of I am
only going to do projects that
are aligned with the most common
themes.
You know, maybe that pays
dividends, but is it really
going to make you happy?
You know, I think the answer
really has to be whichever area
or field of data science or
generally in your career, you're
interested in.
You want to follow, you know,
what's in your heart with it.
And I think the core really is
understanding are the projects
you're doing aligned with a
specific area of data science?
Or is it that your perspective
of what you think you want to do
versus what you enjoy doing are
not aligned and maybe you kind
of need to take a step back and
think about, where do I actually
want to take my career?
JESSICA LI: Yeah, totally agree
there.
Our next question is from Tom F.
With the level of technical
skills data science takes, why
do you think there are so many
resumes per job?
JAMES MARTIN: So, again, a very
good question.
I honestly think it's really the
hype.
The hype around data science.
One of the interesting things,
particularly, from my previous
role was you tend to see a lot
of people wanting to transition
their career towards data
science.
And it's all based on kind of
this, like, very narrow, this is
my understanding of data science
and not really seeing the job
industry as a whole.
And that's kind of why I wanted
to anchor on make sure you're
taking time to read the job
descriptions because it really
does give you a sense of what
does data science mean to this
company?
Or what does this role mean for
the data science function in
this company?
And that, then, allows you to
put yourself in a position of,
is this the right direction I'm
going?
But the reality is, data
science as an industry is
growing so quickly that, I
think, everyone wants to be a
part of it.
And with a little bit more work
in terms of how we're tailoring
the approaches that you can
start to see some of the very
highly technical roles that
require a lot of programming may
not be the best suited fit for
some people or even what they
want to do but because it's
called the data scientist, they
might naturally want to move in
that direction as opposed to
well, what roles could data
science be?
And am I more suited to a
different area?
JESSICA LI: Yeah, totally agree
there, James, data science is a
broad field.
And like you said, an important
point that it's also quickly
growing.
So sometimes, these definitions
change day-to-day.
JAMES MARTIN: I felt bad only
putting four titles in the
examples.
I literally could've chosen a
hundred and still not have
covered everything.
It's such a huge space.
JESSICA LI: Totally agree
there.
Our next question from Deepak.
So Deepak asks, most data
science jobs, like you said, are
very different.
So how should we prepare for a
particular data science job?
Should we do the research first
and learn accordingly?
Or should we actually learn
first and then find either one
or many data science jobs
accordingly?
Chicken or egg problem.
Do we do research first and find
job or other way around?
JAMES MARTIN: So, I think it's
kind of a mix of both, right?
My guidance would be at the
very outset of a job search,
start looking at different job
descriptions to get a sense of
what am I actually applying for?
And the idea behind that
message is not, hey, I want you
to invest a ton of time up front
and just waste it all if you
don't get an interview.
It's the more research you do
and the more understanding you
have of the job industry, the
easier it's going to be for you
to target your approach, tailor
your resume and prepare for that
interview.
Kind of what I alluded to,
right?
You can generally prepare for
50 different data science job
interviews.
Because they could all have
different skews or slants on the
specific function in that
company, right?
It's kind of a mix of both.
You know, I think finding a role
that sounds interesting, finding
a company that interests you and
then, just taking the time to
figure out, is this what I can
do?
Can I bring value to this?
And focusing your search that
way.
JESSICA LI: Yeah.
Great answer there.
Another very similar chicken or
egg problem from Jacob Miller
is, so one common theme in
CareerCon is talking about
career switching and building
your portfolio using more online
resources.
Jacob asks, should our time be
better spent on, say, taking
more MOOCs or online courses or
accumulating projects/working on
a portfolio?
JAMES MARTIN: Personally, I
think the open source projects
and building your portfolio is a
great way to demonstrate
practical applications of what
you've done.
I don't see them as mutually
exclusive.
Learning or courses or further
education is building the
theoretical knowledge with which
you can exercise that practical
muscle.
I feel like I'm going for the
most neutral answer here.
But in reality, it has to be a
jewel approach.
It's not like, all or nothing.
Searching for open source
projects you can immediately
dive into, doing research on
them to see, is there anything
maybe just a step beyond if I
did extra course work I would be
able to tackle?
All of that is building
momentum and furthering your
career.
JESSICA LI: I think what you're
alluding to is something I've
also personally observed between
myself and my friends also is
often times, we think of it as,
do we do one thing or the other?
But really it all goes
hand-in-hand together.
Maybe today you start with one
of the things but tomorrow,
it'll be the other.
JAMES MARTIN: It's kind of the
unfair answer, do everything.
You know, it's not just here's
the simple A to B route, it's
kind of breadth of experience,
right?
I don't think it's possible to
be an expert in every single
area.
But if you're able to
demonstrate a core strength in
one of the fields for the role
you're applying for and you have
a broad either practical or
theoretical knowledge, you can
kind of build on that throughout
your career.
JESSICA LI: Awesome.
Great advice.
Our next question from Mike
Lane.
Mike asks, what tips do you have
for a whiteboarding interview?
JAMES MARTIN: So, there's not
really a specific, like you must
do this.
My advice going into any
whiteboarding, just given the
nature of how you're building
everything out.
This goes for any technical
interview, right?
Is that the sole focus is going
to be do you understand the
question?
What is your methodology for
figuring it out?
How independent are you in
coming to this solution?
And are you showing that there
is only one solution?
Or are you able to display
there's different ways that you
can do it?
In the sense of whiteboarding
where you're actually doing it
right there, my guidance would
be just make sure that you fully
understand the question that
you're thinking and taking time.
It's okay to say, can I have a
minute?
And just make sure I'm building
this the right way before you're
diving straight into it.
Kind of the same with most
tests.
Just making sure that you're
taking time to start and that
you're doing it and ask for
tips, ask for help if you need
help.
Again, interviewers are not
there to trip you up.
And a big part of the
expectation of a job unless
you're like at the very end or
whatever.
You're always going to need help
or support or guidance in some
of the projects you're working
on.
So there's nothing wrong with
asking for that in your
interview.
If you understand the core
fundamentals of the question and
confirming or clarifying aspects
of the question, that's
absolutely the way.
JESSICA LI: Yeah.
So we talked a little bit about
MOOCs earlier.
And as a follow-up question to
that, Ken Osborne asks, do you
think that MOOCs are valuable to
put on a resume?
And also, if so, is it better
to list it in education or
experience?
JAMES MARTIN: I guess that
would depend on how you're kind
of relaying what you've done
throughout your career.
If this is, you know, the
primary experience when it comes
to field of data science then,
yeah, I would put that in
experience.
If it's a supplementary piece
where you have appearance as a
data science, I would put it
under.
JESSICA LI: What is a common
mistake that may negatively
affect an applicant's
application the most.
JAMES MARTIN: In terms of the
resume?
JESSICA LI: Yes, I assume so.
JAMES MARTIN: Most common
mistake.
I think it's probably not
changing your resume.
I don't mean changing every
single word.
But I think there's a general
perspective in the market that
there's so many applicants for
every role that it is kind of a
shot in the dark and that maybe
your resume isn't being
reviewed.
So there's kind of less emphasis
on am I presenting information
for the job I'm applying to.
Sometimes, just making sure
you're really trying to
demonstrate.
Sorry, I'm getting -- can you
hear me okay?
JESSICA LI: Um, you're actually
a little bit quiet right now.
JAMES MARTIN: Can you hear me
now?
JESSICA LI: Maybe a little bit
louder.
JAMES MARTIN: Is that any
better?
JESSICA LI: Any chance you can
get a little bit closer.
JAMES MARTIN: Right here.
JESSICA LI: You're still a
little bit quiet for some
reason.
I think it's a little bit better
than before.
JAMES MARTIN: Remind me the
question, again, the most common
mistake on applications.
It's just not tailoring the
approach and making sure you're
highlighting the experience for
that position.
JESSICA LI: Mm-hmm.
Great.
Yeah, I love all of the
questions that our attendees
have been asking today.
Thank you so much, James, for
your really helpful presentation
as well as the Q & A.
James will be online in the
Slack channel offline for more
questions.
So feel free to go and go to our
CareerCon Slack channel and send
your questions there and chat
further with James.
Thank you so much.
JAMES MARTIN: Thank you.
