in Palo Alto, California, it's theCUBE.

Covering Women in Data Science Conference 2018.

Brought to you by Stanford.

>> Welcome back to theCUBE, we are live at

Stanford University for the third annual

Women in Data Science Conference, hashtag WiDS2018.

Participate in the conversation

and you're going to see people at WiDS events in over 177

regions in over 53 countries.

This even is aiming to reach about

100,000 people in the next couple of days,

which in its third year is remarkable.

It's aimed at inspiring and educating data scientists

worldwide and of course supporting females in the field.

It's also got keynotes, technical

vision tracks, and a career panel.

And we're excited to welcome back to theCUBE,

a cube alumni, Ziya Ma, the Vice President of

Software and Services Group and the

Director of Big Data Technologies at Intel.

Ziya, welcome back to theCube.

>> Thanks for having me, Lisa.

>> You have been, this is your first time coming to a WiDS

event in person and your first year here.

You are on the career panel.

>> Yes. >> That's pretty cool.

Tell us about, you just came from

that career panel, tell us about that.

What were some of the things that excited you?

What are some of the things that

surprised you in what you heard at that panel?

>> So I think one thing that was really exciting

is to see the passion from the audience,

so many women

excited with data science.

And it was the future of what data science can bring.

That's the most exciting part.

And also, it's very exciting to get connected

with so many women professionals.

And in terms of,

you know, surprise?

I think it's a good surprise to see

so much advancement in women development in data science.

Comparing where we are and where we were two years ago,

it's great to see so many woman speakers and leaders

talking about their work in the data science space,

applying data science to solve real business problems,

to solve transportation problems,

to solve education, healthcare problems.

I think that's the happy surprise, you know,

the fast advancement with woman development in this field.

>> What were some of the things that you shared,

maybe recommendations or advice.

You've been in industry for a long time.

You've been at Intel for quite a long time.

What were some of the things that you felt

important to share with the audience,

those in-person here at Stanford which is about 400 plus,

and those watching the live stream?

>> Yeah, you know, Lisa, I provide career coaching

actually for many women professionals

at Intel and also from the industry.

And a lot of them expressed an interest

of getting into a data science field.

And they ask me, what is the skillset

that I need to develop in order to get into this field?

I think first, you need to ask yourself,

what kind of job you want to get into in this field.

You know, there are marketing jobs, there are sales jobs.

And even for technical jobs, there are data engineering

type of jobs, data visualization,

statistician, data science, or AI engineer,

machine learning, deep learning engineer.

So you have to ask yourself, what kind of job

you want to move to and then assess your skillset gap.

And work to close that gap.

Another advice I give to many woman professionals

is that data science appears to have a high bar today.

And it may be too significant a jump

to move from where you are to a data science field.

You may want to move to adjacent field first.

And to have a sense of what is it

like to work in the data science field

and also have more insights with what's going on.

And then, to better prepare you

for eventually moving into this field.

>> Great advice and I think one of the things that jumped

out at me was you talked about skillsets.

And we often hear a lot of the technical skills, right,

that are essential for a data scientist.

But there's also softer skills, maybe it's more

left brain, right brain, creativity, empathy, communication.

Tell me, in your ascension to now the VP level at Intel,

what are some of the other skills besides

the technical skills that you find

as data science as a field grows and infiltrates everything,

what are some of those softer skills

that you think are really advantageous?

>> Great question.

I think openness and collaboration

are very important soft skills.

Because as a data scientist, you need to

work with data engineering teams.

Because as a data scientist, you extract

business insights from the data.

But then you cannot work alone.

You have to work with the data engineering team

who prepares the data infrastructure,

stores, and manages the data very

efficiently for you to consume.

You also have to work with domain experts.

Let's say if you are applying data science solutions to

solve a real business problem, let's say in a medical field.

You need to work with a domain expert from the medical field

so that you can tailor your solution towards, you know,

addressing some medical problems.

So you need to work with that domain expert

who knows the business operations and processes

in medical field really, really well.

So I think that's, you know, collaboration is key.

And of course you also want to collaborate

maybe with academia and open source community

where a lot of real innovations are happening.

And you want to leverage the latest technology

building blocks so that you can accelerate your

data science application or solution advancement.

So collaboration and openness are the key.

>> Openness is a great one.

I'm glad that you brought that up.

We had another guest on talking about that earlier.

In terms of being open, one,

to not expecting, you know, in the scientific method,

you go into it with a hypothesis

and you think you know what you're going to find

or you want to know, I want to find this.

And you might not, and being open to going,

okay, that's okay, I'm going to course correct.

'Cause failure in this sense is not a bad F word.

But also being open to other opinions, other perspectives.

That seems to be kind of a theme that we're hearing

more about today, it's be willing to be open-minded.

>> You know, that's an excellent point, Lisa.

You know, I can share one example.

When coming from an engineering background,

when I first moved into this field,

we always had the assumption that

when we talk with your customers,

they must be looking for something that's high performance.

So our initial discussion with our customers

centered around Intel product lineup

that will give you the highest of performance

for deep learning training or for analytics solution.

But as we went deeper with the discussion,

we realized that's not what customers

are looking for in many cases.

The fact is that many of them have collected

a massive amount of data over the years.

They have built analytics applications

and you add on top of that.

And so as the data representations get more complex,

we want to extract more complex insights.

That's the time they want to apply deep learning

but to the existing application infrastructure.

So they're looking for something,

let's say deep learning capability, that can be easily

integrated into the existing analytics solutions stack,

into its existing infrastructure and reuse its existing

infrastructure for lower cost of ownership.

That's what they are looking for.

And high performance is just nice to have.

So once we are open-minded to that learning,

that totally changed the conversation.

Actually, in the last couple of years,

we applied that learning and we have collaborated with

top cloud service providers like Amazon, Microsoft,

Google, and you know, Alibaba and Baidu

and a few others to deploy

Intel-based deep learning capabilities.

Libraries, frameworks, into cloud so that, you know,

more businesses and individuals can have access.

But again, it's that openness.

You truly need to understand what is the problem

you are solving before simply just selling a technology.

>> Absolutely, and that's one of the

best examples of openness that's obviously

in this case listening to customers.

We think we know the problem that we need to solve

and they're telling you, actually, it's not that.

It's a nice to have, and you go,

whoa, that changes everything!

And it also changes, sounds like,

the downstream collaboration that Intel knew we need to have

in order to drive our business forward

and help our customers in every industry do the same thing.

>> Exactly, exactly.

>> So a couple of things that I'd love

to get your perspective on is the culture at Intel.

You've been there a long time.

What is that culture like in terms of

maybe fueling or being a nice opportunity for bringing

in this diversity that we so need in every industry?

>> Yeah, you know, one thing I want to share, actually,

just now during the panel discussion I shared this.

I said Intel will be the first high tech company achieving

full representation of women

and under-represented minorities by the end of this year.

>> Wow, by the end of 2018?

>> Yes, we pulled in our timeline by two years.

Yes, we're well on track for this year.

>> Wow. >> To achieve that.

And I personally, I like this quote from

Brian Krzanich, our CEO, that if we want tech to define

the future, we must be representative of that future.

So in the last few years now, Intel has put great effort

into hiring and retention for diversity.

We also have put great effort for inclusion.

We want to make sure our employees, every one of them,

come to work, bring their full selves for the value add.

We also invest in diverse entrepreneurs

through Intel capital initiatives.

And most importantly, we also partner with academia,

universities, to build the pipeline for tech sectors.

So we put a lot of effort

and we committed about $300 million

for closing the gap at the company

but also for the high tech sector.

So definitely we are very committed

to the diversity and inclusion.

But that doesn't mean that we only focus on this.

And of course, we make sure that our people are bringing

the right skillsets and we bring the most qualified people,

you know, to do the job.

>> On the pipeline front, one of the things I was reading

recently is some of the challenges that organizations

that are going to, say, college campuses to recruit,

some of the missteps they might be taking

in terms of if they're trying to bring more females

info their organization in STEM roles,

don't staff a booth with men, right?

Or have the only females that are at a recruitment event

be doing, handing out swag, or taking names.

Obviously there's important roles to be had everywhere.

But that was one of the things that seems to be,