*APAC Heritage Month* The Asian-American Experience: Past, Present, Future
Christine Hung, VP, Data and Engineering, Flatiron Health, will share the story of her career, and the path she took to be where she is today.
Hello Cloudflare TV and happy APAC Heritage Month. My name is Amada Echeverría and I'm on the field marketing and events team here at Cloudflare.
We're very excited about this special episode of the Asian American experience past, present, and future brought to you by Asian Flair and Desi Flair, Cloudflare's employee resource groups, the mission of which is to inspire and elevate all those who identify as Asian.
As you may know, the United States celebrates Asian American and Pacific Islander community month in May and the leaders of our employee resource group have and their allies have decided to drive the global integration of this initiative and so thank you for joining Cloudflare and giving a voice to Asian communities during our celebration of Asian and Pacific Islander Heritage Month.
And we'll be chatting all month long with inspirational folks like Christine about their insights, experiences, and careers.
So without further ado, I'd like to introduce my guest Christine Hung.
Christine, thank you so much for joining us.
Where are you dialing in from? I am calling in from New York City.
Great, fantastic. So let me just briefly introduce you to give some context for the audience.
So you're currently the VP of Data Insights Engineering at Flatiron Health, which is a technology company focusing on advancing cancer research, for those of you who don't know.
And you're responsible for building scalable decision analytic solutions for Flatiron internally and for Flatiron customers as well, prototyping new products based on customer needs and providing visibility into the patient journey through machine learning and analytics.
Very, very inspiring. And you joined Flatiron in 2019 from Spotify, where you led the data science solutions team to build analytic solutions and develop predictive algorithms with a strong focus on improving and personalizing the customer experience.
And you also previously held leadership roles in data science, engineering, and operations at the New York Times, Apple, and McKinsey, companies nobody has heard of.
So before we dive in further, a quick note for our viewers, if you have any questions for Christine, please feel free to submit them by emailing us at livestudio at Cloudflare.tv.
You can find the banner right below this video.
Wonderful. So happy that you're joining us today.
So we'd like to dig in a little bit into your background and possibly your heritage and your family, if you'd like to share and how this has had an impact on your life.
So can you tell us a little bit about your background and maybe what your parents expected of you growing up?
Sure. So hi, everyone. Really excited to be here.
So I am from Taiwan originally. Taiwan is a very small country in East Asia with roughly 23 million people.
I grew up there in a very traditional family.
My father is a civil engineer and my mother was a teacher before getting married and subsequently quit her job to take care of me and my sister full time.
I went to college in Taiwan.
I studied finance and I moved to the U.S. back in 2006 to go to business school in the Bay Area.
And I currently live in New York City with my husband, my three kids and a dog.
And in terms of expectations from my parents, to simply put it, I mean, it's all about being successful, you know, both academically and career wise.
They wanted me to have the best education possible in the U.S.
They wanted me to work at a big multinational company. It's all about upward mobility, if that makes sense.
I mean, they just believe that better education leads to better job opportunities, a better network and therefore a better chance of being successful in the long term.
My parents were also super protective of me and, you know, had very strong opinions about how I could be successful.
So, for example, growing up, I was always very interested in math and science and I did very well.
But, you know, in high school, when it was time to decide what I wanted to focus on in college or for my career long term, my father strongly discouraged me from going into engineering, even though my father himself was an engineer.
And his view was that engineering was just a difficult place for women to be successful and or at least is at least true for his generation.
And he just wanted an easier path for me.
So I ended up choosing finance at the end, which was supposedly a lot more female friendly, or at least in Taiwan.
And but after studying finance for four years in college, I still wasn't sure if it was really the right thing for me.
So I joined McKinsey as an analyst to just get a broader perspective and to learn more about different industries in order to figure out what I really wanted to do.
But I ended up working on mostly finance projects anyway, because that was my background.
And eventually, I switched into sales operations when I was at Apple, and totally unexpectedly got a taste of engineering through various projects.
And I just ended up building a career in data science.
Okay, so interesting how in the end, in trying to not be an engineer, you ended up becoming one.
Yes, it was destiny. So great, thank you for sharing. So you touched a little bit on gender expectations and this view that your father had about women going into engineering.
Can you tell us a little bit about how gender expectations affect your interactions with family and friends?
So according to my mother, my father was apparently very disappointed when he found out that I was a girl when I was born.
You know, in those days, like they couldn't figure out the gender of the baby in advance.
And of course, my father denied it outright.
He said he never said anything like that. However, this story was told so many times in my family, and it just really stuck with me.
So at a very young age, I made up my mind that I will prove to my father that I will be much better than boys.
And it sounds very silly, I know. But it actually has been a really strong motivation for me, at least throughout my teenage years until college.
You know, just this feeling that I wanted to and I have to prove to my dad that it was better for him to have a daughter like me than to have a son.
And I know at this point, you think that my father is a little bit sexist, but he is not.
He is actually a feminist.
If anything, he's the one who really pushed me hard to always do better, you know, to compete with the best regardless of gender.
And he talked to me about like the importance of being financially independent, for example, you know, as early as I can remember, which means that having a good career, having a strong trajectory and, you know, not having to rely on a husband.
He also taught me the importance of challenging assumptions and challenging authorities.
He taught me how to think independently and make decisions on my own.
And most importantly, he really made me believe that I could do anything if I put my mind to it.
So back to your question, I don't know if gender expectations really play much of a role in terms of how I behave or how I interact with others.
If anything, you know, I often try to challenge norms and try to shape things up when I can.
Yeah. And it sounds like in your family, the emphasis was placed on achievement and career regardless of gender.
So it makes sense that in and of itself, gender didn't have, wasn't the sole influencing factor.
So that's very interesting.
Thanks. Thanks for sharing. And what about career versus marriage expectations?
Do you have to make any trade-offs? Did you have make any trade -offs about that early in your career?
Sure. So first of all, I consider myself very lucky because my parents actually had zero expectations for me when it comes to marriage, right?
So like no talk about like, you know, you're old, like you need to get married, nothing like that.
You know, they really just wanted me to go as far as possible in my career, which I know is not the case for a lot of folks, I think, especially in the AAPI community.
So when I graduated from business school at the age of 28, I basically set a goal for myself, which was that, you know, I would focus on my career for the next five years and really not worry about my relationships at all.
And in those five years, I never once pressured my boyfriend at the time, who's now my husband, to get married.
And I also didn't put any pressure on myself about when I need to have children and all that, even though I knew that eventually I wanted to have children at some point.
Yeah, so basically, I just punted the question completely and allow myself the freedom to not have to worry about it, which was very liberating.
And then, you know, of course, fast forward, when I turned 34, which is when the clock started ticking for me, I got married, I got pregnant four months later, and I am now a mother of three.
My son was actually born in February, and he just turned three months old last week.
So to answer your question, no, I did not have to really make a trade-off earlier in my career.
Or I guess you can argue that the trade-off that I made was that was career first and marriage family later, which I know is not for everybody, but it really worked well for me.
Great. And I think it's fascinating that there were no expectations about you when it came to marriage, which might run contrary to a lot of stereotypes about certain immigrant communities.
So I like that your narrative is also breaking stereotypes in many ways.
And I think it could be just my parents were very picky.
They were like, no, no guys are like good enough, you know, for their daughter.
Until I met my husband. I like that. Great. Yeah, no, no one's ever good enough.
So congratulations for having your third child, by the way. Thank you. And, you know, shortly after having your child, thank you for being with us here today.
And so speaking of being a mom, can you tell us more about how you integrate motherhood and career?
Sure. So for me, I think the most important thing, like number one thing is to actually marry someone who is supportive of your career and really wants you to be successful.
In my experience, that's really the foundation that you need to integrate motherhood and career.
I think without a supportive partner, you probably feel very stuck.
I think especially if your partner or your family is constantly pulling you away from work, right, which could make you feel that and like making you feel like you have to choose one or the other.
My husband and I, you know, we met in business school and he really values my independence.
And it's important to him that I have a great career. So for both of us, really, you know, having a fulfilling career is a must for a healthy relationship and a healthy family.
And the question there is really just, you know, how we can make this work.
For me, the fact that I didn't get married until my mid 30s actually helped in many ways.
And I know it's, you know, sounds very counterintuitive.
So when I was pregnant with my first kid, I was already 35. I was managing a big team already.
And at that point, I had the autonomy, I had the flexibility at work.
And I was also financially just better positioned to get more help.
I guess at the end of the day, I know that every individual situation is different.
And I think you just need to figure out what works best for you and for your family.
People often ask me like, hey, you work so hard, do you feel bad not being with your kids?
You know, are you missing out on their childhood? And my answer is no, I don't feel bad.
And in fact, I feel very good about having a career, you know, like being busy.
Because first of all, I really want to model for my two daughters, and my son to that women can absolutely have a fulfilling career, have multiple children, and a happy family.
And secondly, I would say, you know, like, I just know that I am not a good mom when I'm with my kids 24 seven, you know, I had three kids, I went out three maternity leaves.
Like all the time, I was just miserable, I was just like thinking about work and wanted to go back to work.
And I know that many women actually feel the same way, but they are just afraid to say so.
So you know, just to, I guess, you know, break the norms, I'm going to say again, I am not a good mom when I'm with my kids 24 seven.
And I think that is okay.
I need time for my own, I need time to build a career. And I need to have intellectual conversations and being able to really challenge myself.
And I think that actually makes me a much better mom when I'm with my kids.
Right, thank you.
So let's switch topics a bit and talk about your career growth. How did you get into data science and engineering?
And is this something you plan for? Yeah, so I did not plan for this at all.
But I'm really glad that it worked out. So my first job out of college was to work at McKinsey, you know, like I mentioned earlier, because I didn't really like finance, and I just like wasn't sure what I was going to do.
So I thought consulting would give me like a broader view into different types of roles, different functions and in different industries.
However, because my background was in finance, I guess, thanks to my father, I just basically got pulled into all the finance related projects.
The most important topic at the time in Taiwan, you know, in the financial industry was risk management.
So I worked on a lot of risk prediction algorithms back in 2004, 2005, dealing with really messy data, and just like really had the opportunity to get my hands dirty before it was cool to do so.
And later, like I mentioned, you know, I was actually trying to move away from finance and working revenue generating initiatives.
And my role in sales operations and analytics at Apple led me to work with engineers to automate a lot of processes that that were just done manually by my team.
And also, you know, we were thinking about how we can build a lot of the dashboards, dashboards, you know, just like automate these manual process.
And so I just had to, in that project, I had to learn to speak the technical language, you know, having discussions with engineers about data modeling, you know, data pipelines.
And fast forward, I've been leading data science teams, you know, since 2013.
So my learning from this is that I think it's really important to embrace the challenges that are coming your way, because, I mean, you just never know what what's going to turn into.
I remember complaining to people about having to become more technical than I expected to be.
But that experience actually ended up opening up so many different opportunities for me.
I mean, I wouldn't be leading data science teams if I didn't put it so if I wasn't put on the risk management projects to build algorithms.
And, you know, if I didn't have to work with engineers at Apple, right, I really don't think, you know, I can be where I'm at today.
So in hindsight, I'm just really glad that I didn't shy away from those challenges.
Right? Yeah, it's when you you feel challenged that you're growing.
So yeah, exactly that that's when you're like learning the most.
And to me, that's just sort of, you know, time and time again, every time something big comes out.
And I'm like, okay, what it like, should I really take this challenge?
Is this the right time? And I always remind myself, like, you know, remember how this like worked out really well for you, you know, it probably will do that again.
So fantastic. So AI and machine learning. These are some of the most popular fields these days.
What common misconceptions do people have about these fields, or the field?
I know they're used interchangeably sometimes?
Yes, yes. Yeah, I think probably like two things I would call out and share.
I think the first thing is that, you know, like, I think everyone should know that AI or machine learning, like it's not rocket science.
I know it's been kind of talked as if this is magic, and that it solves all the problems.
Like, it's really not like that.
I would say the concept is actually quite straightforward. It's basically, I mean, mostly pattern recognition.
And I thought it would be fun, maybe for us to just like do a little quick exercise to illustrate this, you know, I think it will be interesting.
So I'm going to ask you, do you know how to figure out whether someone is a dog owner?
And what will be the size that you will look for?
Not to put you on the spot, but just like, you know, I don't know if you have dogs, but I thought it's a sort of common thing for people to relate to.
I think when I hear someone talking in baby voices, they don't have any kids, no children.
So I'm like, who are they talking to? Yes, yes. I think that's definitely one.
I can think of other things like I'm a dog owner myself. I always have dog hair on my clothes.
I mean, you can't see it right now. But like, you know, it's always everywhere.
You know, dog owners also just like often have poop bags in their pockets, right?
Because they have to pick up the poop. That's just what you have to do, especially in New York City.
Dog owners show the photos of their dogs all the time.
And, and you know, the list goes on, right?
And if you think about this process that we're just going through right now, you know, thinking about what are all the signs that can basically predict if someone is a dog owner, right?
That that's sort of the idea of machine learning, like this process, you know, this pattern recognition process that we're doing.
I mean, basically, if we try to teach a computer to look for all the signs that we just talked about to identify or predict who is a dog owner, by feeding all the data to it, it's called machine learning, right?
So as you can see, this is actually a very pretty straightforward concept.
And then you can use basically the same logic to think about other things.
So for example, and how do you predict if someone has a kid, you know, if you look at my Spotify listening history, you will know that I absolutely have kids, you know, they listen to wheels on the bus, they listen to all these kids songs that adults just don't listen to.
Or, you know, how do you predict if someone's gonna is in the market to buy a Peloton, right?
There are all these prediction problems that machine learning can help solve.
And again, it's it's recognizing the patterns of human behavior, but using computers to learn this behavior extremely quickly, and really efficiently.
And I guess in terms of misconception, I guess the second thing that I would say is that, you know, I know a lot of people just like, throw this term AI machine learning around as it's really going to solve all our problems.
But it really won't solve your problems until you understand how it works, right.
So but but I do think that machine learning is going to become more and more important for our day to day life.
And I would really encourage everyone to to embrace it.
You know, every industry is trying to leverage AI more.
And I think it's really important, not just for technical leaders, but for business leaders as well, to spend the time to understand these core concepts, so that you can better position your team to leverage the power of machine learning.
Okay, point taken. Final question. Can you share some advice to folks who are eager eager to get into this field?
Yes, of course. Um, I think I think the most important thing number one is to really think about what your end goals are when you say that you want to get into AI or get into machine learning.
Because machine learning is a technique.
I mean, it's a way to solve problems. But it's not a goal, at least not for me.
So I think instead of saying that, oh, I just want to get into machine learning, because that's the future, because that's what everyone else is doing.
I would encourage you to fill in the blanks at the end of that sentence, with a problem that you're passionate about that with a problem that you really want to solve, right?
So you'll be something like, I want to get into machine learning to solve the problem of highway congestion, I don't know, or, for me, you know, in my case, I want to use machine learning to solve to help doctors make better clinical decisions.
And that's actually the reason why I joined fire and or whatever that is for you.
I think the second thing is, it's, it's also important to know that machine learning really cannot solve every problem.
And because sometimes it's just not the right solution.
Sometimes you can have much simpler solutions to solve a problem faster rather than trying to fit machine learning into into that framework.
And that is totally okay. I think for business leaders, I would I would really encourage them to like just really learn the basic concepts of machine learning.
I think that really goes a long way. I mean, you don't have to write code, you don't have to be the person like doing the execution.
But you should understand these concepts so that you can more efficiently communicate with your technical team and bring the right use cases for them to solve.
And lastly, I would say I know there are a lot of folks kind of thinking about switching into into AI into machine learning and build a career there.
And for those folks, I would say, you know, to the extent possible, take a junior role in machine learning, you know, get your hands dirty as early as soon as possible.
And I can assure you, if you're focusing on solving the right problems, your efforts will pay off very, very quickly.
And it will really open up many new career opportunities for you.
Fantastic, Christine. So thank you so much for your time today. And thank you for sharing and being so open with us.
And yeah, your stories. It's so inspiring. You know, not every career trajectory has to be completely linear.
I like the twist and turns and how open you were to growth and change.
So thank you again. And folks, please stay tuned for more APAC Heritage Month programming brought to you by Asian Flair and Desi Flair all month long on Cloudflare TV.
And Christine, thank you again.
And have a wonderful day. Thank you so much. Take care. Hi, we're Cloudflare.
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