(upbeat music)
>> Announcer: Live, from Stanford
University. It's the Cube.
Covering the Women in Data
Science Conference 2017.
>> Welcome back to The Cube.
We are live at Stanford University.
At the second annual Women
in Data Science Conference,
this great, fantastic, one
day technical conference
and we are so excited to
be joined by Yael Garten
who was one of the career panelists.
Yael, you are director of
data science at LinkedIn.
Welcome to The Cube.
>> Thank you, thanks for having me.
>> So exciting to have you here.
Everybody knows LinkedIn.
My parents even have
probably multiple LinkedIn
accounts but they do.
You serve 400 plus million accounts.
I'd love to understand,
what is the role...
What's the data scientist's
role in the business overall?
>> Yeah, so I guess when people
ask me about data science,
what I love to kind of start with is...
There are a couple different
types of data science.
So I would basically say
that there are two main categories
by which we use data science at LinkedIn.
If you think about it,
there's really data science
where the product of your
work is for humans to consume.
So, using data to help inform
business or product strategy
to make better products,
make more informed decisions
about how you're investing your resources.
So that's one side, which is
often called decision sciences
or advanced analytics.
Another type of data science is
where the consumer of the
output is really a machine.
Right, so rather than a human, a machine,
so basically these are things
like machine learning models
and recommendation systems.
So, we have really both of those.
The second category typically
called data products
and so, we use those in
virtually everything we do.
So on the data products,
much of LinkedIn is a data product.
It's really based on
data. Right, our profiles,
our connection graph, the
way that people are engaging
with LinkedIn helps us improve the product
for our members and clients.
And then we use that data internally
to really make better decisions
to understand, how can we better serve
the world's professionals
and make them more
productive and successful.
>> Right. Fantastic.
So tell us a little bit
about your team. It sounds
like it's sort of broken
into those two domains.
You must have quite a
large team or lean team?
>> Yeah, we have, the way we have our team
is that we work really closely
within all of our product verticals.
And we embed closely with the business
to really understand what are the needs.
And then we work very cross-functionally.
So we will typically have, in any group,
a product manager, an engineer,
designer, data scientists,
often it's from both
kinds of data scientists,
so sort of more on the analytics side,
more on the machine learning side.
Right, marketing, business operations,
so really very cross-functional teams
working together, using this data.
>> Very smart. Very
integrative from the beginning
really kind of by design.
>> Yes.
>> So that collaboration
is really sort of natural
within LinkedIn.
>> Yes.
>> That's fantastic. Very progressive.
Certainly something that
everybody benefits from.
>> Yes
>> Right, because whether you're on
the advanced analytics side or on
the machine learning side,
you're getting exposure
to the business side, vice versa,
which that's really a great
environment for success.
>> Yeah, and part of what
I love about LinkedIn
is actually our data culture.
And how data is infused in the culture
of how we do things. Which
is not always the case.
>> It's not. And it's cultural shifts...
We were talking about that
with a number of guests today.
Especially, depending on the
size of the organization,
That's tough.
>> Yes.
>> So, to have that built
in and that integration
as part of this is how we
do business is really...
you can imagine all the potential
and possibilities there.
So, would love to understand
how is LinkedIn using data
to recommend ways to evolve
products and services
to best serve all of its members?
>> So, maybe two different examples
of how we do this. One is...what we do is
every launch that we have, every feature
that we generate, we really do it
in an online experimentation setting.
So, we have a certain feature
that we're about to
roll out to our members,
we want to make sure
that it's a better
experience for our members.
And better, as measured by the metrics
that we've defined, in terms
of measures of success.
And so, which is really aligned
to what value we believe we're delivering
our members and customers.
And so when we roll out features,
we'll roll it out to a certain
percentage of our users,
test the downstream impacts of that,
and then decide, based on that,
whether we actually roll that feature out
to 100% of members.
And so, that's one of
the things that my team
is heavily involved in,
is really helping to use
that data to make sure that
we are structuring things
in a way that's statistically sound
so that we can measure
the impacts correctly
of rolling out certain features.
So that's kind of one category of work.
And the other category is really to do
sort of opportunity identification.
And kind of deep dive insights
into understanding into
a certain product area.
Where are there opportunities
to improve the product?
So, one...let me give
you a high level example.
One of the ways we might
use data is to say okay,
are certain members, in certain countries
accessing via IOS or Android?
And if so, should we be developing more
in differentiating between
IOS and Android apps
is one simple example,
where we'll actually decide
our R&D investments based on
the data that we're seeing,
in terms of how people
are using our products
and do we think that
that's important enough
of an investment to make
to improve the products
and invest in that area.
>> Wow, very very smart.
What are some of the basic ways
that data scientists
can deliver more value
for their stakeholders, whether they're
internal stakeholders
across different functions
within the organization or the members,
the external stakeholders?
>> I think one of the
most important things is
to really embed closely, into these
kind of functional or domain areas.
And understand qualitatively
and quantitatively,
what's important.
Alright, so understanding
what the business context is
and what problem you're trying to solve
and I think one of the most important ways
that data scientists play a role
is actually helping to ensure
are we even answering the right questions?
So, as an example, product
manager might ask data scientist
to pull certain data or
to do a certain analysis.
And part of the
conversation in the culture
has to be, what are you trying to get at?
What are you trying to understand?
And really thinking through,
is that even the right
question to be asking
or could we ask it in a different way?
Because that's going to inform
what analysis you do,
really how you're delivering
the results of this analysis
to make better decisions.
So, I think that's a big
part of it, is having
this iterative process
of doing data science.
>> Really, it sounds like
such an innovative culture.
And you're right, looking at the data
to determine, is this the right next step?
Is it not? How do we maybe adapt
and change, based on really
what this data is telling us.
If we kind of look at
collaboration for a second.
You talked about the integrated
teams, but I'm wondering,
how do you scale
collaboration within LinkedIn,
Across so many businesses
and engineering stakeholders?
>> Yeah. So the way I
like to think about it is,
you have to invest in
culture, process and tools.
So let me start from the bottom up.
So on the tools or technology,
one of the ways to do it is actually
to create self serve tools, to
really democratize the data.
So, first of all, investing in foundations
of really good data quality.
Right, whether you're creating
that data yourself or you're collecting
that externally from
different organizations.
Once you have really good data quality,
making sure that you have foundations
that enable self serve data basically.
So, for example. Some of the things
that data scientists are used today
in various companies really
doesn't need a data scientist,
if you've invested in ways
where a business partner,
let's say can quarry that data themselves.
They don't need a data
scientist to be doing this roll.
So that's an important investment
on the technology side.
In addition, making data
scientists really productive
by using...investing in
tools that will enable them
to access the data is really important.
So once you have that sort of technology,
it enables your data
scientists to be productive.
The process is really important.
So, just as an example, we
have a sort of playbook,
in terms of, how we launch features.
And part of that is,
bring in data insights,
in terms of, which features
we should be building.
And then, once you've
determined, using the data
and those insights, it's okay,
how are we going to launch this,
in terms of, experimental
design and setting?
And then, what are the success metrics?
How are we going to know that
this is actually a good feature?
Then once we've launched the
experiment, analyzing that.
Where all the stakeholders
are part of this,
the product manager, the executive,
the engineer, the data scientist.
And then, kind of iterating on the results
and deciding what the decision is.
So having actually a
process that the whole team
or the company, abides by, really helps
in having this collaboration
where it's clear
what everyone is doing and kind of
what's the process by which we use data
to develop and to innovate.
And then, finally culture. I think
that's such an important part
and that really needs to be
from bottoms up, top down, everywhere.
It really needs to be a
community and a culture
where data is discussed
and where data is expected
and where decision making
really is grounded on data.
I fundamentally believe that
any product being developed
or any decision being made
really should be data informed,
if not, data driven.
>> Right, absolutely. One of the things
that I'm hearing in what
you're doing is enabling
some of the business users
to be self-sufficient.
So you're taking that feedback
and that input from the business side
to be able to determine
what tools they need to have
and how you need enable them so
that you've got your resources
aligned on certain products.
>> Yeah, just as an
example, one of the things
that we do, for example,
is we realized over time
that this isn't actually productive
and how do we ourselves scale?
So we started doing data
boot camps, for example.
>> Okay.
>> Where we'll actually train new people
coming into the company, on data,
and our self serve tools, and
on how to run experiments.
And so a variety of different aspects.
Even how to work with data
scientists productively.
So we actually train that.
So this data boot camp really helps us
to instill a data culture and
it really empowers the team.
>> So this is anybody coming in,
whether they're coming
in for a marketing role,
or a sales ops role, they
get this data boot camp?
>> Yeah, and it's open to anyone.
Typically it's going to be a
certain subset of those people
but it really is open to anyone
and we're talking about more
ways of how do we scale that
and maybe how we put on LinkedIn learning
and make that more broadly accessible.
>> Yeah. So you have quite a big team.
How do you keep all of the data scientists
that you've got, happy?
What are the challenges that they face?
How do you evaluate what
those challenges move forward
so that they have an opportunity
to make an impact at LinkedIn?
>> So part of the things
are actually the things
that I mentioned, right.
So you know, a culture of data.
It's really important in when we see
that this is not happening,
actually addressing that.
So, data scientists are going to thrive
in a community and a culture
where data is valued.
And where data scientists are valued.
So, that's actually a
really important aspect.
You know, luckily people come to us
because they know that we do value data
but I think that that's very
important for any company.
And so, you know, I
advise start-ups as well.
And this is one of the
things that I tell people
that are founding companies is,
you have to have a
culture which values data
to attract data scientists.
Because otherwise, they
have other options.
The other thing is
having these foundations
that enable them to be productive.
Alright, so these tools and these systems
that enable them to
really do high value work.
And invest in the right areas.
So you start graduating from doing things
that are more, maybe repetitive,
or low level, and figure
out, how do you scale that
so that you can have data scientists
really efficiently using their time
for things that only they can do.
>> Right. I love that this
culture is sort of grooming them.
One of the things that, a
couple things I read recently.
One was that, I think it was Forbes
that said, 2017, the best job to apply for
is data scientist, but
from a trends perspective,
it's looking that, by 2018,
it's going to be a demand
so high, there's not
going to be enough talent.
How are...what's your perspective
on LinkedIn, are you...
Have you...sounds like, from
a foundational perspective,
it is a data driven company
that really values data,
is that something that you
see as a potential issue or...
You really have built a culture of such...
Not just collaboration and innovation,
but education and that LinkedIn
is in very good position.
>> Yeah, so one thing is that...
I didn't mention, in terms of
the happiness factor, right,
is that it is actually a place
where data scientists look for a place
where they can also grow and learn
and be with other
like-minded data scientists.
So, I think that's something
that we strongly support.
Again, for companies that people
that may be viewing this and are not
in such environments, there
are a lot of ways to do this.
So, keeping data scientists happy
also can be facilitating meetups.
Right, with data scientists
from your local region.
So those are ways that people,
you know, share information
and share techniques, and
share challenges even.
Right, because this is a
growing and evolving field.
And so that having that community
and one of the things that's
amazing about this conference,
is that it's creating this
community of data scientists
that are all sharing, you know,
successes and failures as
data science is evolving.
The other thing is that data science draws
from so many different backgrounds.
>> Yeah.
>> It's a broad field, right. And there's
so many different kinds of data science
and even that is getting
both more specialized
and more broad.
So, I think that part
of it is also looking
at different backgrounds,
different educational backgrounds
and figuring out, how
can you expand the pool
of people that you're
looking at, you know,
that are data scientists
and how do you augment
what skills they may not have yet.
On the job or through training
or through online education.
So, we're looking at all these ways.
>> That's fantastic. We've
heard a lot of that today.
The fact that the core data
science skills are still
absolutely vital but
there's some other sort of
softer skills.
You talked about sharing.
Communication has come up
a number of times today
as really a key not only to be able
to understand and interpret the data
from a creative
perspective and communicate
what the data say but, to your point,
to grow and learn and keep
the data scientists happy.
That social skill element
is quite important.
So that was an interesting
learning that I heard today.
And I'm sure you've heard
many interesting things today
that have inspired you as well.
>> Yeah, and that's
something that you know,
creating this culture is something
that even data science
leaders around the world
were discussing this and
talking about this, you know,
what are the challenges and
how do we evolve this field
and how do we help
define and kind of groom
the next generation of data scientists.
To be in a more stable, maybe better place
than where we were and help
to continue to evolve it
and so it is...yeah.
>> Evolution is a great word.
I think that that's another
theme that we've heard today.
As much as I'm sure you've inspired
and educated these women that are here,
not just in person
today, but all the 70...
70 cities and 25 countries
that's being live streamed.
It's growing, it's amazing!
And I'm sure that they've
learned a ton from you
but it's...probably just in the little bit
that we've had time to chat, I'm sure
that you probably gleaming
a lot from them as well.
>> Yeah, definitely.
>> We're scratching the surface.
>> Yes. Absolutely. So there
are many more years to come.
>> Exactly. Yael, thank you so much
for joining us on the Cube.
>> Thank you.
>> It's a pleasure talking to you.
>> It was a pleasure.
>> We wish you continued
success at LinkedIn.
>> Thank you.
>> And we want to thank
you for watching The Cube.
We've had a great day at the second annual
Women in Data Science Conference
at Stanford University.
Join the conversation, #WIDS2017.
Thanks so much for watching.
We'll see you next time.
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