Cloudflare TV

Yes We Can

Presented by Michelle Zatlyn, Solmaz Shahalizadeh
Originally aired on 

A recurring series presented by Cloudflare co-founder and COO Michelle Zatlyn, featuring interviews with women entrepreneurs and tech leaders who clearly debunk the myth that there are no women in tech.

This week's guest: Solmaz Shahalizadeh

Solmaz is the Vice President of Data Science and Engineering at Shopify, leading the teams responsible for leveraging data and machine learning to reduce the complexities of commerce for over one million businesses worldwide. During her time at Shopify, she built the company’s financial data warehouse, played a critical role in their successful IPO, implemented and scaled the company’s first machine-learning products, and led multiple cross-functional teams. In addition to this, she champions a company culture that makes ethical, data-informed decisions and is passionate about building high-quality data products to improve the user experience.

Prior to this, she worked at Morgan Stanley as an analyst and McGill University as a cancer researcher, where she applied machine learning techniques to predict breast cancer outcomes.

Women in Tech

Transcript (Beta)

All right. Hi, everyone. Thanks to everyone for joining for this week's Yes We Can.

I'm so honored to have Solmaz here. And Solmaz, actually, how do I pronounce your last name for the audience?

It's Solmaz Shahalizadeh. Okay. Shahalizadeh.

Pretty good. Thank you. You're a good teacher. That's great. Excellent.

Good. Well, I'm so honored to have you here. Solmaz is the head of data science and engineering at Shopify, one of the true great success stories of the Internet.

And so great to have you here today, Solmaz, to hear more about some of your awesome projects you've been working on.

So welcome. Thank you very much. And thanks for having me, Michelle.

I'm a big fan of you as well, and really excited to be here today.

That's good. Well, I love that part of my job is I get to talk to great people like you.

So this is so fun. Best way to spend my Wednesday morning. And so let's dig right in.

So currently, you run data science engineering at Shopify.

And data science is an increasingly popular word that I hear a lot.

And it's a bit of a mystery to many people, especially if you don't work in the industry.

And I actually think even within the tech industry, it's a bit of a mystery.

So maybe we can start with just, you're an expert. Can you share with the audience, what is data science?

Yeah, I agree that the term is kind of ambiguous. So my understanding and my definition of data science is that it's an interdisciplinary field that looks at using computational methods, statistics, at times software engineering to bring insights and knowledge from lots of different data sets.

So at times, this data might be unstructured, they might come from disparate sources.

And the idea is that using the technical tools of your toolbox and bringing insights and knowledge out of that, and maybe to share how we use that in Shopify and how I do that day to day, I can go a bit more detail.

So at Shopify, we use data science engineering is embedded into every aspect of what we do and into every area, every product we have, and every service we offer has a data science component with it.

So we use data, we look at the data on commerce, we see how our users are using our product, how our merchants are basically benefiting from the tools we're giving them, and where are the gaps and what we can offer.

So by looking at data, we inform what is the next best thing to build and how we can also improve the things we have built.

That also sometimes means using technologies like machine learning and artificial intelligence to build new product opportunities to build products that are now solely possible because we have data and because we have this skill set.

I'm curious how you use data science in Cloudflare.

I love that. I feel like the panacea you described where you use the data science to come up with new products is almost every company's dream.

So I want to come back to that. At Cloudflare, yes, we absolutely do as well. I mean, we offer cybersecurity to our customers.

And so one of the things we've done from a really early day is how do you crowdsource all that threat data from our large set of customers to identify who's legitimate or an illegitimate visitor to provide better cybersecurity to everybody?

So early on at Cloudflare, we used to describe Cloudflare as a neighborhood watch, like every new person in the neighborhood watch it gets better.

And it was really because of the data. And we have a saying internally that at the end of the day, cybersecurity is really a data game.

The more data can solve it because you can use all these machine learnings and AI models to say, hey, this is likely a threat.

Stop it. So it makes everybody safer.

And one of the things that I love that sometimes I'm constantly amazed at is that means we have 27 million Internet properties using Cloudflare daily.

We stop 45 billion cyber attacks on behalf of those customers.

And it's not people. It's the data science that's doing the work behind the scenes.

Yeah, it allows you to scale like solutions very easily using data.

So yeah, it's a great field to be. Oh, that's so great.

That's great. I mean, okay, so we all understand a little bit better.

So thank you for giving us that lesson, Solmaz. Maybe you can share one or two projects where you use data science, either to create that new product or some new thing.

And it could be at Shopify or even in your personal life, just to maybe even contextualize this a bit more.

For sure. So actually to make sure everyone knows what Shopify does, Shopify is a commerce platform allowing merchants, small and big, to bring their businesses online, offline, wherever their buyers are, and help them to sell anywhere.

And we have over 1 million merchants. So as a result, we also have a lot of data.

We see back offices of businesses. We see their storefront.

So it's a very comprehensive set of data that we have viewed for our merchants.

And our data philosophy is always that the data of merchants belongs to merchants and we use them to make their experiences better.

So one of the ways we have used this data is for a product we offer to our merchants, which is called Shopify Capital.

So the way that product works is that we basically give cash to our merchants to help them grow their business.

And you can imagine traditional institutions often ask for a lot of documents, a lot of detail before they're able to offer this to entrepreneurs.

And sometimes for new entrepreneurs, they are just starting.

They don't have all of that ready to go. And being able to have you into data and being able to understand their businesses better, Shopify is able to offer merchants capital at way faster than any other institution and earlier in their business.

The way we do that is by building probabilistic models that allow us to predict the success of the merchant and their future sales so that we can offer them money that we think they are likely to return as they succeed.

The other part of capital that's kind of cool is that the merchants only remit money and get money back when they make sales.

So in a sense, we are putting our success in their success.

So it was a really interesting project because it was one of the first times that we said, okay, as a public company, we're going to give real cash to people based on probabilistic models.

And it meant that we had to really work hard to understand the domain of the product, what are the factors involved there, but also build a product that can scale fast.

Because you can give all of these factors to a person, similar to what financial institutions do, and they can go through all of those factors.

And maybe in a week, they can give offer to one or two people.

But we wanted to scale this for over 1 million merchants. And having data and algorithms with the right checks and balances in place allowed us to scale this product really quickly.

So that's one where machine learning and data is the core product and core to how it functions.

But there is a lot of data work also behind the scenes, deciding what would be a good thing to build.

So for example, there are times that we see, for example, in the beginning of the pandemic, we started to see that there is an appetite for local delivery of things that buyers buy, or people want to have a pickup in store quickly.

So by looking at the data and looking at the signs of that, then we were able to share these insights, and then our product team was able to build it.

And now over close to 40% of our brick and mortar merchants in English speaking countries have used this product and are using local delivery.

So again, in aggregate, we're allowed to have this view into commerce that allows us to react to the needs of the merchants and the world really quickly.

I love that. And then I would even say, and then you're empowering entrepreneurs everywhere, right?

You're helping people with these ideas come to life in really smart, efficient ways.

So especially now during this pandemic, it's great that you've been able to empower all these, I guess you call them rebels.

Arming the rebels.

Arm the rebels or empower the entrepreneurs. But it is true. I mean, I feel like for a lot of small businesses, the odds are stacked against them.

And it feels like Shopify is building the tools. And it sounds like you're using a lot of data science to take things that used to be really hard and make it easier.

So to set these entrepreneurs up for success. Yeah, definitely.

Because if you think about it, like a large company, Shopify, Cloudflare, we're able to have teams of data scientists.

So when we want to understand their business better, we are capable of having data scientists, data platform engineers, infrastructure to dig into our data, understand something, make decisions.

But we also want to sort of scale this and give it out of the box to every entrepreneur that joins Shopify.

And that's what we are doing. So they don't need to have to hire a team of data scientists or data platform engineers.

They log in and they see insights in their home dashboard about like what product is selling well, what's the next best thing they can market.

And I think that's really empowering to be able to give that to anyone anywhere in the world, just because they have joined Shopify.

Yeah, that must be a good recruiting. When you're recruiting for your team, I mean, that must be a really good to say something that you might not realize is this because that feels really good making a huge impact around the world.

Across the world. Yeah. I really like it because you can feel the impact that you have very easily.

And I think in a world that many things are changing during the pandemic, it's good to have that sense that what you do today can make someone better and has that potential to reach many people across the world.

That's great. That's great. I've heard you speak before about how part of your job is to ask questions and then go figure out whether they're true or not.

And I just when you said that, like, I smiled to myself.

And I mean, because it sounds like fun.

You know, you have to basically dream up questions and go decide what go check the data to see whether they're true or not.

So maybe what's a recent example of a question that popped into your head at some hour of the day that you went and then check the hypothesis against them?

And what and then did it turn out to be true or not?

Yeah, we have a very recent example, actually, that I think would resonate with a lot of people.

So as the pandemic started, one of the questions that I think across the world everyone had was that, how is commerce going to be impacted by this?

Because the buyers are being financially and economically impacted all across the world.

And something that's near and dear to shop by how entrepreneurs are going to be impacted.

This is a time that they would be scared to take a risk and start something new.

Because, you know, we all know entrepreneurship has its own set of hardships, or is the time that people say, like, no, it's the time to follow my idea and follow my dream and start my own business.

So the early days, we would, we were, we thought that, or I thought that we would see less sales, we would see buyers spending less time, less money buying things, and also people taking less risk, and you would see a drop in entrepreneurship.

So we started looking closely, and we would, you know, the beauty of Shopify is that we have merchants all across the world.

So we started looking at the areas where there was a serious lockdown, like Italy, Spain.

And what was really interesting was that we saw the number of new businesses or new merchants coming on the platform in the areas of lockdown actually go up.

So for time after time, we're like, something must be wrong, right?

Like, we must be missing something.

So that's where, like, you see the data, but you almost want to prove yourself wrong until you have no reason to be wrong.

So we started digging into this more and more.

And we saw, no, actually, in these areas, people are going to entrepreneurship because they might have lost their traditional work, or they may think that this is the time to start something they want.

So it was really surprising when in the second quarter, we actually saw close to 70% increase in the number of new businesses coming online during time.

Yeah, exactly. It was. So, I mean, it's good news for entrepreneurs and good news for Shopify, and we're happy to be here to provide that platform.

But it was quite surprising. The other part was that we say, okay, so people are still, the buyers are still spending money.

What was interesting was to see how their purchases were changing.

So initially, we were seeing, for example, a lot of food, beverages and essentials being bought, then it went into crafts and hobbies and whatnot, and then how the sales continue.

So that was also really fascinating to be able to observe that.

We also saw things like, initially, the brick and mortar sales went down because people couldn't go to stores.

And initially, the option of like going in curbside pickup or things like that were not available.

And then in June, we saw those things starting to come up.

So I think like that was really surprising. It was a good surprise, but it was still a surprise to see that what we didn't know, if things are like entrepreneurs are still going to think about starting businesses, and you see that happen.

That was really impressive. I love that. That's I love that story. And, you know, as you describe all these different trends you've seen, I'm sure there's others on the call, the audience, because the first thing I thought about, hey, this is really cool.

Is there a place I can go look these things up? Do you publish this on a blog somewhere?

Yeah, listen, can go read about this? Is there somewhere that you can point?

Yeah, for sure. So we publish those as part of our Shopify blog.

We put them, we also like our merchants and like people to be informed of these trends.

So part of it is that like for our merchants, they can see them and around their own business and how it's doing because their business.

But then for across commerce, we have published some of these things and we have disclosed them.

Yeah, quite fascinating trends, actually. It is on your blog. So is that the I think it's slash blog, but yeah.

Shopify .com slash blog.

Great. Excellent. Well, I'm going to go check that out. So it is fascinating to see that.

I love that. Okay, so switching gears. So you're a technical leader and you're a technical leader at Shopify, this very successful high growth company, you're growing very quickly.

And so you hire many people for your team, right?

It's not just you doing this. You have a team that you run. And so just like data science, hiring is a skill.

And so I'd love to, can you share with the audience how you think about hiring for your team and maybe how that's changed over time?

Yeah, for sure. We are continuously hiring. We continue to hire even now, if anyone is interested.

And I do spend quite a bit of my time in hiring and building the team.

So what I have learned through the years is that it's better when we hire for a potential that someone has rather than like checking the exact university degrees.

In fact, we don't check for degrees in university education at Shopify.

But what we ask for is sort of what are the skills they have and what are the things that they aspire to grow into?

Especially in data science, even we spoke in the beginning, the field has a vague definition.

There are some data science programs here and they're popping up.

But the reality of what I look for when I hire is if someone has a curiosity about the problem we're solving, and then they have the technical skills to follow those curiosities.

Because the other aspect you have to keep in mind is that so many technologies that we use like Spark, Beam, whatnot, they are very new.

So you can't find someone with like 10 years of experience in Spark.

It's kind of not possible because the technology didn't exist.

And also there is no guarantee that in future, we're going to use the same thing.

So more than what they know at the time we hire them, I'm more interested in their capacity to learn and capacity to follow that curiosity.

I love that. One of the things that I was told along the way is the rate at which you learn is kind of like becomes a superpower.

And I try and tell that to everyone because you're right, it's not about knowing everything on day one.

It's how fast can you learn? Are you a sponge?

Are you curious? Do you want to be a good teacher? Do you want to be a good learner?

I think that's great that you look for that. I think people are probably in the audience that part of it how, hey, something like Spark is a technology we leverage a lot in our team, and it hasn't been around that long.

I mean, that probably scares some people who are like, what, you're using something that hasn't been around a lot longer?

Like, how do you stay current in all these trends and the new tools?

And how do you decide which ones to use or not to use? Because it is moving at a fast pace, especially in your field where it's changing and kind of developing, emerging and quite kind of emerging while you build your team.

Yeah, that's a good question. So like, we adopted Spark in 2014. And I always joke that like, Internet didn't know how Spark drawings worked at the time.

So we sort of grew with the pains of the open source. But in Shopify, and in my team, we're strong believers of contributing to open source community.

So we always have appetite for trying new things and contributing back.

So even if we don't end up using them long term, we do have a high appetite for supporting the community.

So that's on the side. But in terms of how we keep up to date, I think you're right, technology is changing a lot.

Every platform you build, you have to start about thinking, rebuilding, refactoring it almost the moment you finish the migration.

It's the reality. That's good to hear. It's not just that Cloudflare.

That really makes me feel better that it's not just at our company. So thank you for telling me that.

No, I think it's a cross, right? Because by the time you pick a technology, you sort of bring it, you scale it to your needs.

And immediately the business grows as well, right?

So we are in this lucky position similar to Cloudflare that we are still growing.

So what we paid for the next three years, by the time we have hit three years, then some new capabilities are possible.

So like, I don't have a silver bullet. But what we do is we keep an open eye to the technologies.

And then also open eye to what our true needs are. Because the other thing that you want to stay away from is trying technologies just for the sake of technology.

Because any new thing you bring, you also have to keep thinking about how you scale it, how you maintain it.

So I think having a balance of focus on the needs of the product or problem you're solving, and versus knowing like where the technology is going, I think that's the right balance to have.

And you know, there are times that we go sort of stronger in one end than the other, and then we have to pull back ourselves.

So I think it's going to be like that for many years.

And it's okay, because I find like, you know, despite the jokes on Twitter, the community has pushed it so much further.

And I still think that it was a good choice to adopt it and learn and modernize our technology, because going from where we are to the next version is going to be much easier if we had not done this transition.

Because your team also gains the skills during this time of how to like switch gears and focus on something new.

But it's a very long journey.

And I think in general, in tech, we have to talk about that part of it a lot more.

Like we hear a lot about new technologies that come, or like how do you move from an open technology or last version, and what it takes to I think it's something that we can speak about more openly across the technology.

That's great.

I love that. I feel like your answer there is a very realistic explanation of what decisions you have to make on a daily basis and technical teams have to make on a daily basis of how to invest their time and resources and what tools to bet on and how do you contribute back?

Like, I think that's a really very realistic answer of what I've experienced at Cloudflare anyway.

So thank you so much for sharing that.

You know, one of the things, you know, and it ties back to this idea of data science emerging and all the tools emerging is, I've also heard you speak about how many professions use data science, right?

It's many professions embedded.

And so my understanding is you're also pretty open-minded from the backgrounds and where people come from for your team here at Shopify.

Maybe you can share any learnings that you can share with the audience of how that's worked out?

Because I think there's probably lots of people who could learn from you because you've been willing to do that.

Maybe share a little bit more about that point of view.

For sure. So, you know, data science as a term and as a field is fairly new when you compare it to things like engineering or product or design.

So one thing that we learned early on is that there are many other fields, interdisciplinary fields, that have had to deal with massive amounts of data that is unstructured, that's really hard to get insights from, and they have had to work through it to come up with insights and conclusions.

So some of that is what I studied before, which is bioinformatics using computational tools to understand biological phenomena and processes better.

So I had to go through like microarray data, lots of data that needed to be normalized and clean and all of that so we could get predictors of outcomes for diseases out of it.

So that sort of opened up, because the other part of it is that like in your disciplinary fields, you also have to be able to explain the results you get to people that don't have a computational background.

So for example, you would have to, I would have to explain the findings that I got from these computational techniques to biologists, to oncologists.

So that sort of opened our eye and then we saw this a lot. So we hire a lot of people with astrophysics background.

Just in general, physicists are amazing.

They have lots of, they are some of the smartest people, but they also have lots of patients to go through data and come up with results.

And being able to sort of have that lens across our hiring for data science has helped us a lot, because we don't look at traditional backgrounds.

We have people that come from mechanical engineering, they come from biomedical engineering, all of those fields that have had to deal with data, even economics, they are fantastic.

And they really bring a new lens.

And you also see they are able to tell the story of data really well to an audience that might not know anything about data, because at the end of the day, like the audience doesn't have to care about data, they have to care about what they learn from the data.

And I find these people coming from interdisciplinary fields have to do that time after time.

And that has allowed us to hire really well working.

I love that. Now that you say it, it makes so much sense, but that would have been, I would have had a different hypothesis before you gave me the answer.

So thank you for sharing that. That kind of makes me rethink, hey, how should we approach this for some of our different teams?

So thanks for sharing that.

Okay, I want to go back to what we were talking about before, we have about 10 minutes left, is Shopify is such a great company.

I mean, I love that you're empowering entrepreneurs around the world.

It's not just in North America, it's literally around the world.

You talked about Italy and Spain. And during this pandemic, we need more short stories, happy stories like that, more than ever this year, I feel like 2020, it's been a very gloomy year.

So we need more stories like this, and we need these businesses to succeed.

So maybe like, I'd love to hear one or two stories of maybe entrepreneurs who got into business during this pandemic or whose business they've either pivoted or altered their business and really doing well during this time, just kind of as a motivational inspiration for all of us, can you maybe share one or two stories of some entrepreneurs you're seeing using Shopify?

Yeah, for sure. We are really blessed that we get to hear these stories, because I agree with you, it's been a hard few months and being able to see that you have been able to change the course of someone's life is really gratifying.

And one of the ones that is near and dear to my heart is this place in Ottawa in Toronto, it's called Burgers and Fries.

And then they were able to come online for the first time, using Shopify and sort of survive through the pandemic being able to connect with their buyers.

And it has allowed them to also not be confined to the geography of where they were previously located as a physical place, and then be able to like have a broader audience and their products and their services reach beyond what was possible before.

And it's also been fascinating to see the resilience of our merchants, because it's, you know, we are here to serve them.

And what has been really eye opening is how resilient entrepreneurs are, like they start doing things and say, okay, this is something that the merchants are doing, and we should find a way to support them better and better.

Another thing that we saw was a merchant that was able to get Shopify capital during the pandemic can grow their business during a time that has been very hard for them to get that help from traditional business, and then just see their business flourish during this time and being able to see those merchants, it's quite something.

Yeah, it is quite something.

Thank you for that. Again, it's been a very cloudy 2020 and pandemic and all the racial issues in the US around the world, that even now, I'm in California, there's basically many forest fires raging today.

So that's topic.

So it's great to hear these stories of entrepreneurs doing amazing things. So thank you for sharing that.

Okay, before Shopify, you worked at Morgan Stanley, huge company.

And, and when you joined Shopify, it was early. I mean, today, it's a big success story over $100 billion mark cap.

I mean, you've just, the Shopify team has done an amazing job executing, but when you joined, it was still a relatively early company.

Was that a hard decision? I mean, there's so many people who work at big companies who want to do what you did so much, they want to go join the company and be part of making it happen.

But there's all the what ifs, what if it fails?

What if it isn't successful? How did you get like, was it a hard decision for you?

And people who are thinking through that decision of going from a big company to a growth, a growth tech company?

Yeah, definitely going from a bigger company to a startup or smaller company has its own set of questions that come up.

I think I was able to find a company that works in a problem space that I was really passionate about, like to me, commerce is, has existed before us and will exist after us.

So it's a very interesting domain. And it's very boundless.

There's so many things that can be done. So Shopify, I was lucky that Shopify was in Canada, and I was lucky that they, there was this problem domain that I was really interested in.

And one of the things I noticed when I went from a large company to a smaller one was the autonomy I got.

So, and I want to learn from your experience as well, Michelle, but like, you know, when you're in larger companies for every single change you want to do, you have to go through change management and emails and tickets and whatnot.

And it might take weeks before the change that you have made sees the light of day, if it ever does.

And then I joined Shopify and on day three, they're like, yeah, you can ship your code.

And I shipped the code and I actually broke production for data warehouse.

But I still have a job because with autonomy comes also tolerance for mistakes and for learning things.

And I did find that really interesting. So I find like, by the time you join a smaller team, you have more autonomy.

You get to wear more hats and try different parts of the role, which when you go to a larger company, things are more specialized and you might not get the same opportunity.

And the other part is that like, of course, there's an element of luck with every startup, right?

Like for every hundred that make it, hundred that start, maybe one or two make it.

So I think that's a realistic thing as a data person to think about. But what I see is that even if you go through the experience of a failed startup, you still learn a lot.

Like when I'm interviewing people, if I see someone that has been in a startup that hasn't gone well, it actually is almost an attractive feature because it shows that they know how a real product works, how you have to have product market fit, how it matters, how quickly you ship a product for your merchants or for your users.

And I think like that actually brings a good dose of reality, especially in fields like machine learning and AI, when there's a lot of buzz out there.

But the reality of shipping something in data products is quite different.

It means like taking care of all the details. And I find people that have gone through those experiences that didn't result in success actually bring that lens and they often add a lot to the team.

So I think if you're not sure, I think it's a good chance to take because I don't think also I'm unique in thinking that experience is valuable.

I think across the companies, people say that. You have taken even a bigger jump, Michelle.

So I'm curious to see how you felt when you did.

Oh, well, I was just that I agree. Again, I think you've said it very articulately, Somaz.

And I would, you know, you described about when things don't work out or you've had to live with decisions.

And I call that scar tissue. And I love people with scar tissue because you learn a lot more, like where you're given this autonomy, right, to go do something and whether or not it works out, you learn a lot through that and you build up scar tissue.

And you're like, okay, it's almost like an intrinsic, okay, next time I'm going to do it this way, especially if you're a really curious person who wants to continue to do great things.

And like, I love people with scar tissue. You're right. Like sometimes you learn a lot more from the things that don't work than you do the things that work.

Sometimes you things work and you don't know why it works. It works, but it's hard to pinpoint exactly why.

And, and, and, but when things don't work, you're like, oh, wow, if I could do it differently, I would try it this way next time.

So, so I think the scar tissue is something that I talk a lot about internally where not about not working out, but it's like trying to make a decision, live with it.

And I want you to live with it for a long time, because I think through it's that time period that you learn both the good things and the bad things and almost everything you're making in the daily decision, there's good and bad.

And even with, there's things that come along with it. Okay. You're constantly kind of refining and tweaking it.

And I think that's how you really grow and expand in your career.

Yeah. Okay. We, we are down to 90 seconds. And I asked, this has been amazing.

So thank you so much for being so generous with your time. I I'm having, I I've learned a ton and I really am inspired by, by your stories.

And I I've asked every guest who've come on to yes, we can the same question.

So I'm going to end here is, you know, you're, you're a woman in technology and I would love to hear where has the industry kind of met your expectations and where, or maybe exceeded and where has that, where, where have they, where has it fallen down for you?

Okay. So to make sure we finish on a positive note, let me get started with areas where there can be improvement.

And I think it's generally in technology, but also like across different industries, there has been a lot of work, but there's still room for growth and making things better.

One thing that I see, especially in tech is how sometimes the patterns of excellence and capability that we think and we recognize are really shaped by like the majority of sort of like men that we have seen, how they might represent being skillful, how they might represent being leaders.

And I find like in two stages of people's careers, this impacts them a lot when they are junior and they're entering workforce and you see two people that might have the same skillset, but the way they describe it and the way they talk about themselves might be very different.

And I think that's where with mentorship, when it would help, you can actually encourage a woman to like be vocal about what they know and yeah.

And be able to like sort of show that without having, without feeling like they're ragging or things like that.

And I think in leadership, I think that's not just limited to tech, but in general, if you even look at the TV shows, if you look at the stories, the patterns of leadership that we think about are often shaped by the experiences of men.

And sometimes they say, oh, if this person didn't act exactly that way, then they must not have that capabilities.

But I do see that improving over the years, like seeing female leaders like yourself is really aspiring that you are yourself and you are a you are very successful.

So that's amazing. And areas that are much, so it's been good is that I find like when you're a woman in tech and you go into a room and there's one more woman in tech, instantly you have a sense of camaraderie.

I find that there is a lot of support.

There's a lot of opportunities that women provide for each other. That's really nice.

And I do see like over the last few years across the field, I see things improving.

So I see people not having panels of women in business, but they have panels of business.

And then there are many female speakers there for conferences.

So that's really nice to see. And I hope it continues, especially like right now with 2020, we do need more of these things.

We do need technology and every industry to be more equitable for all the voices to be heard.

Amazing. Well, Somaz, you're an inspiration for all of us.

Thank you so much for coming on the show.

Thank you for being an amazing everything you're doing, helping empower entrepreneurs around the world, arming the rebels and demystifying data science for us.

Big round of applause to you, Somaz. Thank you to everyone for tuning in. We'll see you next week at Yes, We Can.

And if you have any questions, it's Thanks, everyone.

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Yes We Can
Join Cloudflare Co-founder, President, and COO Michelle Zatlyn for a series of interviews with women technology leaders. We hope you will learn, laugh, and be inspired by these conversations.
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