>> Announcer: Live from Stanford University,
it's theCUBE, covering the women
in data science conference 2017.
>> Hi, I'm Lisa Martin, welcome back to theCUBE.
We are live at Stanford University at the second annual
women in data science conference,
it's a one day tech conference and we are joined
by Julie Yoo who is the founder and chief data scientist
of pymetrics, Julie you are on the customer panel today
so welcome to theCUBE.
>> Thank you great to be here.
>> You have, it's great to have you,
you have such an interesting background.
Neuroscience meets engineering or neuroscience
meets engineering, would love for folks to understand
a little bit more about those two, how they're combined
and also about pymetrics, but give us a little bit
of a background as a woman in the sciences,
how you got to where you are now.
>> As you mentioned, my background's in computer engineering
and I went into PhD program in electrical
and computer engineering because I wanted
to study artificial intelligence,
I was fascinated by the notion of artificial intelligence
so my research topic started in automatic
speech recognition system as in building computers to decode
and decipher human speech and after a couple years,
I got sort of frustrated with the engineering
approach or statistical methods based approach
to improving the existing speech recognition
systems that are out there because
I thought to myself, we're trying to make computers
understand human speech and mimic human function
when we don't really understand how our brain works
and I don't really know exactly what happens
when I listen to you speak and when you listen to I speak,
what is going on, we didn't really have a good sense,
so I wanted to study neuroscience
so I quit engineering and I went into a PhD program
in neuroscience and there I started doing a lot
of neural imaging study, just looking at human cognition
and just figuring out what is going on when people perceive
and process these signals that are out there.
>> And was your idea to eventually marry the two?
>> I didn't really think about it that way
but it just sort of happened, as in my background
in engineering did sort of hone me into doing
some of the projects that I did when I was doing my PhD
and my post doctor and while I was doing all of that,
I just evolved to be a data scientist
without really realizing that I was doing everything
that a typical data scientist would do
and this was even before 2008.
The job title of data scientist wasn't even around then.
It sort of happened because of where I came from and
because of what I was interested in and as I was doing that,
it just ended up being a good marriage.
>> And then there it was.
Tell people what pymetrics is
and what the genesis of this company was.
>> Right, pymetrics is a platform that uses
neuroscience based games and data science
to promote predictive and bias free hiring.
How it became a product was because
I was going through post doc and my cofounder
was also going through business school
and we were both going through the phase of okay,
we don't want to stay in academia,
what do we want to do with our lives?
At the time, we realized a lot of the career lives
and tools that are out there were not scientific
and they were not data driven and we felt
that there is a clear need for a tool
that could actually use all these data that are out there
to help people figure out what they should be doing
in their lives, so we thought that we were uniquely
positioned to use our background in engineering
and neuroscience and build a product
that could actually solve these challenging problem.
That's how we started pymetrics.
>> That's fantastic.
You started about three years ago in 2013,
so really getting rid of some of the biases,
share with us what some of the biases are,
is it test scores, SATs, MCATs, GPAs?
>> Well there are many different kinds of biases
in hiring process right now I think.
There is a preconception of what an engineer
should look like and I think that plays a lot
and when you do go into an interview,
how you look and how you dress,
that adds to the bias, there's ethnic bias,
there's gender bias, and there is bias based
on test scores and what school you went to.
We want to sort of remove ourselves from that
and really get down to what kind of person you are
and are you really, I guess have the right set of skills
to succeed in certain job functions.
We do that by measuring, instead of taking your subjective
like answers from questionnaires,
we do that by objectively measuring your behavior
and these games are based on neuroscience research
so we know that they actually measure things
that we want them to measure like for instance
your ability to pay attention, your risk appetite
on all these things that we think matters
as to what makes you good at certain things
and not so good at some other things.
We use these objective data and data science
and predictive modeling to come up with
predictions as to how good you will be
in a certain career versus some other career.
>> Really an incredible need for that.
So it's game based, it's an actual game that people
will play that will help understand more of who they are
as a person, their behaviors, those patterns.
Tell us a little bit about the invention of the game,
what was it like, who was it for?
>> The games were actually sourced from neuroscience
research community, we did not create these games.
What we did was we actually just took them
from research and medical settings and applied it to hiring
and we know that these are relevant
to measuring your attributes and your personality
so why not use that for hiring and just career advising
because it makes sense, we're trying to measure
your qualities, your soft skills and what not,
why not just use it for something
that could really benefit from these sort of data.
What we did though is we actually made these games,
they're not really called games in research community,
but we made it shorter and we made it more applicable
to the things that we are trying to use it for.
>> Because you took feedback from some of your early adopters
who were saying and maybe it's taking me too long,
maybe some of the recruiters might say,
they gave you some very viable feedback
that have helped you optimize the product.
>> Right and as a data scientist, I always think
the more data the better, but that also means
that people would have to sit in front of their computers
and play an hour long battery of games
and a lot of people were thinking
that it might be just a tad too long
and companies felt that spending 45 minutes to an hour
could be a discouraging thing and people felt fatigue
effect and we could see that in their results
so we ended up making it shorter.
We went from 20 games to 12 games
and we cut it down to 25 minutes long
and I think now we are in the sweet spot,
where we do get enough data but at the same time,
we're not making it an hour long.
>> Right, so this is really targeted for people
coming out of university programs,
whether it's bachelor's, master's, doctorate etc.
and also what type of companies who are looking
to hire, what's your target market for that?
>> I think mostly Fortune 500 companies
because a lot of these companies do hire in large volumes,
so it helps to have us go to these companies
and build their models based off of their employees
and if a smaller company comes along
and they only have 10 employees in the job function,
then it's extremely difficult for us to build a model
based off of their 10 employees,
whereas if it's a larger corporation,
then we can have 200 employees play
and we can build a model based on their data.
So generally large corporations is our target client.
>> I'm curious in terms of some of the data
that you're saying that you're analyzing,
are you seeing, we look at data science
as a great example, the event that we're at.
A report from Forbes recently said this is the best job
to apply for in 2017, we're looking at now,
what's going to be happening predicted
over the course of the next year
and that's a shortage in talent.
Are you seeing with some of the data
that you're taking in, are you seeing things
that are mapping to that, like people that are really geared
towards that or are you seeing more companies
that are looking for computer industry,
data science type roles, is that increasing as well?
>> I think companies are definitely looking
for more data scientists and also people are figuring out
that there are data science programs,
graduate school programs and I think the supply
of data scientists is definitely increasing
but at the same time or more so,
the demand for data scientists is increasing.
Not to mention the available data that's out there
is increasing at a faster rate than anything else.
Yeah, it is I think the best time
to be a data scientist right now.
>> Let me ask you one more question about looking at skills.
We have such a great cross section at this event,
of leaders in retail, in obviously what you're doing,
in the neuroscience gaming merging world,
we've got professors here, data science is such
an interesting topic, it's obviously very horizontal.
From a skillset perspective, the traditional skills
of being a statistician, mathematics,
being a hacker, a lot of the things that we've been hearing
around the show today and really aligns
with what you're doing is more the behavioral
insight side of, you have to be able to communicate
what you're saying and be able to apply it.
I'd love to understand the profile of an ideal
data scientist that you guys are seeing from your data,
what are some of the other behavioral attributes
that maybe are some of the non-teachable things
that you're seeing that really come up as this
would be a great career path for someone.
>> I think personally, I think intellectual curiosity
is number one and they would have to have
strong self motivation and discipline
because you could love analyzing data and you could just be
doing that for how many days, I don't know, and that's it.
You need to be able to actually come up with a good story,
you got to be a good story teller
and if you have artistic flair to make the data beautiful,
then even better but it is important to go
from beginning of the project where you have a bunch
of data set and actually come up with actionable results
that people can use and you're not only always
going to be communicating with a data scientist,
so you do need to be able to present your data
in a more succinct and easily digestible way.
>> That sounds like, as the chief data scientist
for pymetrics, that's who you're looking for
to hire on your team, give us a little bit
last question here, just a little bit
of an overview of what your data science team
looks like at pymetrics as you're helping
to leverage this data to give people opportunities
with careers, what does your team look like?
>> Our team has very diverse backgrounds,
we have a few PhD's in physics and,
I have a PhD in neuroscience and there's
other data scientist with PhD in physics,
we actually have one guy who majored in data science
and we have another guy who majored in bioengineering
and yeah, so it's definitely diverse background,
but general theme is that you do need
a good quantitative foundation, whether it's engineering
or physics, it is helpful to have that statistical
or analytical mind and if you can actually apply that,
and actually love solving problems
then I think data scientist is a right role.
>> You're on the career panel at WiDS 2017,
is that the advice that you would give
to the next generation of kids that are interested
in this but aren't quite sure
what industry they would want to go into?
>> What industry?
I think, again if they're even remotely interested
in going into data science, I would encourage them
to pursue it, I think it is one of the most
fascinating fields right now and there is never
going to be a shortage of needs for data scientists,
so if you like it, if you think you are going to be
pretty good at it, I say go for it.
>> Fantastic and you've got a great audience here,
this is being livestreamed in 20 cities
I think across the globe or 75 cities,
I have to get the stats right,
but there's a big opportunity here to be an influencer
and we thank you for spending some time with us today,
best of luck on that panel.
>> Thank you.
>> Thank you for watching, I'm Lisa Martin,
we are live with theCUBE at women in data science 2017
#WiDS2017, stick around we'll be right back.
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