Jon: 00:00
This is episode number 603 with Christina Stathopoulos, analytical lead for Waze and adjunct professor at IE Business School. Today’s episode is brought to you by Pachyderm, the leader in data versioning and MLOps pipelines.
Welcome to the Super Data Science Podcast, the most listened-to podcast in the data science industry. Each week, we bring you inspiring people and ideas to help you build a successful career in data science. I’m your host, Jon Krohn. Thanks for joining me today. Now let’s make the complex simple.
Welcome back to the Super Data Science Podcast. Today I’m delighted to be joined in person by Christina Stathopoulos, a remarkably eloquent communicator of technical and philosophical content alike. Christina has worked at Google for nearly five years in several data-centric roles. For the past year, she’s worked as an analytical lead for Waze, the popular crowdsourced navigation app owned by Google. She’s also an adjunct professor at IE Business School in Madrid, where she teaches courses on business analytics, machine learning, data visualization, and data ethics. Previously, she worked as a data engineer at media analytics at giant Nielsen. She holds a Master’s in Business Analytics and Big Data from IE Business School and a Bachelor’s in Science, Tech, and Society from North Carolina State.
Today’s episode will appeal to a broad audience of technical and non-technical listeners alike. In this episode, Christina details geospatial data and open-source packages for working with it, her tips for getting a foothold in a data career if you come from an unconventional background, guidance to help women and other underrepresented groups to thrive in tech, the hard and soft skills most essential to success in a data role today, and her book a week challenge with her top data-centric book recommendations for us. All right. Are you ready for this fabulous episode? Let’s go.
Christina, welcome to the Super Data Science Podcast. We’ve been talking about getting you on the show for years. Now you’re finally here in person in New York. Thank you for making the trip. Where did you travel to my apartment in New York from?
Christina: 02:27
So far, I’m just north of you, Upper West Side. It’s a travel down. Very, very excited to be here. Like you said, it’s been a long time coming. So I’m excited to be here today.
Jon: 02:38
Pandemic made things complicated for us.
Christina: 02:40
It did. It did. But we made it happen in person.
Jon: 02:42
We were committed to the in-person episode. And so, we had to both be in New York at the same time. Now it’s worked out. I am so excited. We’ve got a great episode planned. So many amazing questions. Even just prior to filming Christina and I haven’t been able to stop talking because she has so many interesting things going on. So let’s dig into it.
Your route into data has been somewhat unconventional. So you’ve spoken before of making it against what you call all the odds, building this career in another country and in a second language. Since your experience as an expat in Spain could be inspiring to other aspiring immigrants all over the world, could you provide us with a cheat sheet that could fill in how other people can have this level of success that you have?
Christina: 03:34
Yeah. To give a little bit of background, I was born and raised in North Carolina, first of all. I’m from the US. I then went and lived abroad in Madrid, Spain right after finishing college, my bachelor’s. I completely developed an analytics career abroad. So like you said, these advice for other immigrants, aspiring data scientists, especially those against whatever odds there may be. So developing this career in a new country, in a different language, in a second language that I had to learn from scratch. I went there without any Spanish-speaking abilities.
Jon: 04:11
Really?
Christina: 04:12
Yeah, yeah, yeah.
Jon: 04:13
How did you pick Spain? You were like, “It seems nice. The weather seems right for me.”
Christina: 04:17
It’s not a bad choice. I mean in the grand scheme of things, I don’t think it was a bad choice. There was a lot of thought that went into the decision, a lot of different factors. But originally I considered going to Asia, but then thought about the language. Knowing Korean, Japanese, how much is it going to take me later in life? I realized that Spain was a good choice. Learning Spanish and being fluent in Spanish would always be a benefit for me. So I moved abroad partly to pick up the language. I’d only planned on being there a year, by the way. A year turned into two, which turned into 10. Things just happened. I mean there’s a lot of lessons to be learned in this. I think it’s an interesting journey, path, especially, like you said, for immigrants.
So a couple things that I learned that others can put into practice. One thing is definitely to find the positive in every situation. So especially when you’re going up against odds and, in this case, not knowing the language, not knowing a lot of people, being young, and not having experience in the field. So finding the positive in every situation, no matter how negative. So turning the tables and thinking, “What can this teach me? How can I learn and how can I grow?” and not having it pull you down. So especially towards the beginning, when I first got started there, there was a lot of negativity in my mind. I needed to get past that. You need to change-
Jon: 05:55
Negativity about just like, “How can I make it here? I don’t know anybody. I don’t know how to speak the language. How am I possibly going to make it work?” that kind of thing.
Christina: 06:03
There’s a lot of struggles. Just like in life in general, getting set up a new life somewhere, you have a lot of struggles.
Jon: 06:10
I can understand that. I mean I’ve only immigrated to places where speaking English. So I’ve moved to England, Singapore, and the US now. I’m originally Canadian. Even though in all of those places, English was a primary language, it’s still absolutely a struggle. Not having a network in place can still lead to that negativity. I completely understand. So in your case, it must be exponentially more difficult.
Christina: 06:40
And culturally too, which you probably experienced going to the UK. It’s very different going to Singapore. You need to adapt to the culture and you need to learn. So there’s this very steep hill at the beginning and you need to just make sure that that doesn’t get you down. Like I said, find the positive, learn from it, and grow from that. So I think that’s definitely one thing that people need to get into that mindset. Then another thing that I recommend is to set up a plan. So think of your timeline. Where do you want to be five, 10 years down the line? Consider how you’re going to get there.
There’s this quote, I don’t know exactly how it goes, but it says that your future is defined by your daily habits. Small daily habits create your future and create your success. So taking into account where you want to be five, 10 years from now, consider what changes can you make today? Tomorrow? What habits, healthy habits, can you pick up to get you on the right track?
So I think that’s another thing that can definitely help, especially for those just getting started in data science, or feeling a bit out of their element immigrating like I did. So that would be the second thing.
Then one other thing that I would add is just to learn how to thrive outside of your comfort zone. So especially for immigrants, or someone in a new environment in general, maybe you’re just moving to a new city, but you are going to be pushed out of your comfort zone, learn how to thrive in that setting. This can be very valuable, I think, for anyone just getting started in data science or making a transition within data science. You’re going to have situations. You’re not always going to feel like you know what’s going on or you’re the expert in the room. You don’t have to always be. But learn to thrive in these situations, learn to go on with confidence, learn to learn from every situation.
Jon: 08:47
Nice. Those are great practical tips and concisely said. So to summarize them back to you is to get past the inevitable negativity of the immigration struggle. It’s just going to happen. It’s going to feel that way. So get over it. The second thing was make a five to 10-year plan, and then adjust your daily habits to nudge you slowly in the direction of those aims. I love that. That is perfect and in line with a lot of what we’ve talked about on Five-Minute Friday episodes on the show. Then, finally, learn how to thrive outside your comfort zone. I love that. Is there anything, Christina, that you would’ve done differently if you could go back and do it all again?
Christina: 09:27
It’s a really good question, and the answer for me is that no. There’s nothing that I would go back and change. I’m not a big person of regret. Again, trying to avoid the negative. I think that everything that happens literally does happen for a reason. I’m afraid that if I went back and changed something, I might not be where I am today. So all of the positive, all of the negative that happened to me before, the struggles, the success, it got me to where I am today. So there’s, in my case, no reason to go back and change it.
Jon: 10:00
God, that’s nice. I live with so much regret. Everything’s a regret. So speaking of success and where you are today, you are currently analytics lead at Waze, a crowdsourced navigation app by Google. This is an app that is familiar to me. I don’t own a car, I almost never drive a car, but I am super familiar with Waze anyway, because when I take an Uber or Lyft around New York or anywhere else in the world, they are often using Waze. So they’ll frequently actually have two phones. They’ll have one phone that is the app they’re using, so Uber or Lyft, and then they’ll have a separate phone just for running Waze. I guess for people in the know, like drivers, professional drivers, they know that Waze is the best app for finding the quickest way from point A to point B.
So very popular for those listeners who haven’t heard of it. Probably most of you listeners drive, and so maybe already use it. And so, you’ve transitioned into several roles during your nearly five years at Google. Now currently into this analytics lead role at Waze. And so, Waze is … It used to be a separate company, but then it was acquired by Google now. So it’s Waze. It’s part of Google. And so, what challenges have you faced as you transition between all of these different roles at Google?
Christina: 11:22
First of all, great intro to Waze. I don’t think I could have done it better myself. So, yeah, like you explained, I’ve been with Google for about five years now and I’ve made transitions during these years. To give a bit of context, I started at Google Spain while I was living in Spain. So out of the Madrid Spain office. I started in an analytical consultant role, mainly working when we consider data. I was working with Google Ads and Google Search data and working with our top advertisers to help them with their strategy to use data, to drive their strategy depending on changing search trends.
So I started off in this role, and there were definitely a lot of the typical challenges as I was making these career moves within Spain, the typical challenges, I mean, understanding how I can grow in my role, what is next. As I was exploring this, about three and a half years into this position, into a couple smaller changes within the org in Spain, but I decided to make the big change to come to Waze out of New York City, where I’m currently based, working as an analytical lead. It was a big transition, not just in the sense of the move. I had to now move from Spain where I had been for about 10 years, come back closer to home in the US.
So besides this typical big change that I needed to make, I need to make this physical move, I also had … Having to adapt to a completely new company and a completely new way of thinking with data.
This is where I want to go into detail. But the transition of broadening my data science scope and now going into geospatial. This was a huge transition for me and also a huge challenge that I needed to confront, and I needed to learn something completely new. I had very, very little experience with geospatial data prior to working in Waze. We dabbled a little bit with it during my master’s. I studied a master’s back in 2015 in business analytics and big data. But besides that, I had never worked with geospatial data. So by joining Waze, I now had to learn how to analyze and how to understand geospatial data in the sense of how are people moving? How is it changing? What are the macro trends looking like when superimposed over a map, for example?
So what did this mean for me in the analytics world? I had to learn a lot of new things.
So to give a really simple example, when I first came into this new role and working with new teams, one typical use case you might have is you might want to understand how much has a user driven from point A to point B and what distance have they covered during the day? What is the average that people are driving during a week? So you would think, okay, you just measure the distance from point A to point B, right?
Jon: 14:31
Right.
Christina: 14:32
It’s not that simple, of course. You have to take into account the curvature of the earth.
Jon: 14:37
Oh, of course. So you don’t think the world is flat?
Christina: 14:40
No, I do not.
Jon: 14:41
Okay. Very good.
Christina: 14:42
Not in the flat earth group.
Jon: 14:45
How many flat-earthers are there at a geospatial company?
Christina: 14:49
I haven’t met one yet, but I’m not going to …
Jon: 14:52
Not going to pry.
Christina: 14:54
Yeah. Yeah.
Jon: 14:57
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Christina: 15:34
So, yeah, this is a really simple use case. You have to take into account the curvature of the earth. It’s actually a little bit longer than just a straight line between point A and point B.
Jon: 15:43
Right. Unless they’re driving really far, and then the curve really matters.
Christina: 15:48
Yeah, yeah. The longer you go, the more that curve might actually matter. So this was a use case that I had to wrap my head around. How do we do all of this? So, thankfully, there’s already been a lot of work done in the geospatial realm. There is something called S2, S2 Geometry. This was created by Google. It’s this library of geospatial … It allows us to picture data on a 3D sphere.
Jon: 16:21
It’s open-sourced, right?
Christina: 16:22
It’s open source, yeah. So this is completely public. It’s open source. It allows you to have data on a three-dimensional sphere versus a two-dimensional projection on a map. So it is much more accurate, of course. It’s much more in tune to how the earth is shaped. If you go into it a little bit more in detail as well, it’s much more accurate, but it’s not perfect, because the earth is also not a perfect sphere. It might even be more like elliptical. But that’s a conversation for another day.
Jon: 16:58
So you’re an elliptical-earther.
Christina: 17:00
No. That’s a conversation for another day. It’s not perfect, but it’s getting there, the sphere way of looking at the earth and data pictured this way. So we use this S2 library to be able to make these analyses and calculations as people are moving throughout over the years. And so, with this, the fact that it’s able to picture the earth in 3D, it has functions that allow you to compare different geographic objects and the relationship between them.
So to go into a little bit more of a complicated case, rather than just driving point A to point B, imagine, for example, that we want to understand all of the Walmarts across the US and we want to locate all of the Walmarts that are within three miles of a school. So you want a list of these. If you didn’t have a way to optimize this query, then you would literally have to compare every Walmart across the US with every school and make matches.
Jon: 18:07
It’s like a traveling salesman problem, kind of.
Christina: 18:09
Yeah. It would be around along those lines. It’s not optimal. It could be a crazy … Performance-wise, if you do it that way, it’s not going to work. Well, it will work, but it will take a lot more processing and time. And so, you can use these S2 libraries to optimize the query and it will allow you to use what is called S2 cells. So S2 cells break the earth into different polygons, geometric shapes, and classifies everything within these geometric areas. You can use the S2 cells, the S2 functions to then optimize a query that will match the Walmart locations with schools, but using the ones that the system already knows are within a reasonable geographic distance.
Jon: 18:56
That’s cool.
Christina: 18:57
You’re not comparing all of them, but you’re narrowing down the matches that you’re going to test for.
Jon: 19:03
Cool.
Christina: 19:04
So all of this was completely new to me a little over a year ago, and I was having to learn it from scratch. It was incredibly interesting actually and opened my mind, broadened my mind to just a new realm of analytics. Then as well, for listeners to know, you can access a lot of this straight through BigQuery in the Google Cloud platform.
Jon: 19:25
Oh, no kidding.
Christina: 19:26
Yeah.
Jon: 19:26
I love BigQuery.
Christina: 19:27
If you search for geography functions, you’ll be able to find a good help page that explains them. Within the functions, you’ll see reference to S2, and it’s using this system. So it’s something that you can play with and do on your own. Then also just to add, and I’m not as familiar with this, but there’s another system. It’s called H3 by Uber. It’s the H3 Hexagonal Spatial Index. Uber did something similar, created their own system, but with a different approach. As you can imagine, Uber has a lot of use cases similar to this. So they created their own system at the time. So you’ll hear a lot about the usage of S2 and H3.
Jon: 20:09
Got it. Cool. Well, I didn’t know any of that before. So that was all super interesting. It’s great to know that I could be accessing it right away if I needed to be.
Christina: 20:18
Yeah, absolutely.
Jon: 20:19
Nice. What does it mean to be an analytics lead? What does that title mean?
Christina: 20:25
Yeah, it’s a pretty … I get that question a lot, like, “What do you do?” the typical question. So what do I do? In my case, I’m helping lead analytics studies and strategy, again, at Waze, working on the monetization side of the app. So working with advertisers and helping them understand, in this case, how changes in mobility, how that might affect different clients’ businesses and also their ad spend or how they’re placing the ads, in our case, across the map. I did something very similar when I was working at Google. I mentioned that before about how I was working with Google Search and Google Ads. So working with advertisers. So I maintain that same type of job, but now in the geospatial realm.
Jon: 21:13
That’s cool. It makes a lot of sense. I didn’t fully understand the relationship there. I thought you might be super well-suited to Waze in working with geospatial data because you’re great at data analytics, data science, you’re a good critical thinker. But indeed you also get to take advantage of the advertising knowhow that you had from before-
Christina: 21:30
Exactly.
Jon: 21:31
… and client management, that kind of stuff.
Christina: 21:33
Yeah, a lot of the client management as well, and then being able to translate insights from data or trends in data to actual business use cases and/or how it affects our strategy and their strategy.
Jon: 21:44
Right, right, right, and to be able to concisely summarize things in a non-technical way for non-technical audience, to be able to say … Because they’re probably not often interested in S2 Geometry.
Christina: 21:55
No. What’s crazy is I had to learn all about all of this, but I rarely have to speak about it. This is one of the first times I’ve ever really spoken about it publicly.
Jon: 22:08
Oh, nice. Well, it seems like you know it very well. I’m very delighted that you decided to talk about it on air with us. So something else related to what you’ve been doing at Google. So I think it’s no secret that women in tech can have it harder than men, as, I guess, can any underrepresented groups.
Christina: 22:27
Yes.
Jon: 22:27
So at Google, you facilitated an initiative for empowering women and other underrepresented groups to speak openly about their accomplishments in the workplace and beyond, thereby breaking modesty norms and glass ceilings. Was it hard for you to do this? So if these kinds of modesty norms are in place and these glass ceilings are in place, I can imagine that it then makes it hard to come up with an initiative like this in the first place.
Christina: 22:52
Yeah. So I didn’t come up with the initiative by any means. The initiative is called I Am Remarkable. It’s quite well-known nowadays. It’s spread all over the globe. It did start out of Google. It actually started as an internal initiative, and then it became this external, public workshop called I Am Remarkable. It’s not just for women. As you nicely mentioned it, underrepresented groups. It’s trying to help underrepresented groups, because underrepresented groups, minorities, in any situation where you are the odd one out, it happens to all of us at some point in our lives, when you’re in these situations, you tend to lose confidence. You don’t feel like you’re getting a voice at the table. There’s just these different things that come into play when you are a minority. It’s about breaking through that.
It becomes even more important …
I guess when we’re talking about the data and tech world and when we’re considering gender, it’s no surprise, as you mentioned, that women are underrepresented in tech. So this happens to us a lot where we are a minority in a group. It happens to me all the time. In my analytics teams, I’m typically the only or one of very few women. When I speak at conferences or I’m involved in networking or panels, there’s very few women involved. It’s not the most welcoming situation, even though I interact with amazing male colleagues. But it does make you feel like you are the odd one out, that you need to try even harder. And so, this initiative that I was facilitating and a part of was to help underrepresented groups and minorities learn to get their voice out there, learn to be more vocal, learn how to sell your accomplishments.
That’s one thing that when you are a minority, you hold back and you just don’t sell yourself as much. I mean that in a positive sense of the word, because it’s not bragging if it’s based on facts. We’re talking about things that you have achieved. The workshop helps to just help you get the confidence and get a voice out there, because ultimately that’s what you need to do to grow in your career. You’re going to have to prove that you’re making an impact and show your accomplishments.
So as women, one thing that we’ve found often, there’s a lot of studies around this. But especially women in a tech setting, you’ll find that when we have to sell an accomplishment, we will oftentimes say we, like a team effort. When really maybe you need to be saying I. What did I do? It’s not always the team. What did you accomplish? So you’ll find women or just minorities will use the word we when they should be using I.
Then another thing that’s related to this as well, and there’s a lot of studies around this too, that when we go to apply for jobs, you’ll find that women or minorities, we’ll be harder on ourselves. We want to have accomplished 100% or 95% of what is being asked when our male counterparts are okay applying when they’ve only hit 50% of the requirements. So we’re putting too much pressure on ourselves when we don’t need to be.
Going back to your question, so you had talked about how did I overcome this? I, of course, have these issues, too. Definitely have the imposter syndrome. I think it gets magnified or it’s even worse for minorities, the imposter syndrome. So there’s lots of advice out there on how to get over it. But for sure, it helps, I guess, to track your accomplishments. Maybe have a weekly journal, look at what you’ve accomplished during the week, review at the end of the month at end of the quarter. But have a tracking of your accomplishments just to see how far you’ve come. It can help in your career to make sure you have regular check-ins with your manager and/or your mentor to go through these accomplishments and maybe how they might contribute to the growth of your career, your professional trajectory. So that’s one thing that you can help.
Jon: 27:15
Nice. That’s a great tip. Actually, we have a Five-Minute Friday episode dedicated to imposter syndrome, what it is and how you might be able to counteract bits of imposter syndrome if you experienced that yourself. So that’s episode number 502 of this podcast. All right. Thank you for that practical advice, Christina, for our listeners on how they are remarkable and how … Yeah, very practical advice on ways that they could be overcoming things like imposter syndrome. So speaking of situations that could cause a lot of imposter syndrome to flare up, you are an adjunct professor at IE Business School. So how did you go from being a student to a professor? That was also in Spain.
Christina: 28:04
Correct. This is also in Spain. This is another long story that I’m going to try to shorten it. This goes into my unconventional journey in the field. I was a student at IE Business School. I mentioned it briefly earlier. I studied my Master in Business Analytics and Big Data back in 2015-2016 at IE. How did I transition from this student into the academic side as a professor? It started while I was a student, like the efforts … Again, these habits that I was driving eventually led into me landing a professor position. So while I was a student, I was very involved with the school. I was involved in officer positions of the clubs. I was a student ambassador representing our class. Lots of different projects. I attended different conferences, networking. Especially the involvement in the clubs, being an officer, I got the chance to host events, to get closer to the IE Business School staff and professors. So I started to open a lot of relationships here.
Then when I graduated and I went on to work, at first I was working actually in SAS Institute, and then Nielsen as a data engineer. During that time, I got approached by new students at the school, asking me to come back as an alumni speaker. So now I started coming back to the school, staying very involved, but now on the speaker side and helping other students.During all of this, I maintained a really close relationship with IE, with the staff, and with the professors. So fast forward from this a year and a half or so later, they had an opening in one of the MBA programs. They needed a professor to give a course like an Intro to Business Analytics and Big Data. One of the professors that was reviewing this at the time, I came to his mind because he had seen me in a lot of events. He knew I had studied there. He knew I was capable. So he approached me and he asked me if I would like to give it a try as a teacher, as a professor, because he believed in me. So I said, well, why not?
Jon: 30:25
You’re an outstanding communicator. This whole time, listening to you speak, I’m in awe. You very clearly get from point to point. So it doesn’t surprise me that somebody’s like-
Christina: 30:34
I’m in awe. I thought I’d do a lot of ums, but-
Jon: 30:36
I don’t know. I’ll point it out next time.
Christina: 30:38
Okay.
Jon: 30:38
That’ll [inaudible 00:30:39].
Christina: 30:39
I try to avoid that.
Jon: 30:40
No, you really did.
Christina: 30:41
That’s good. Great to hear.
Jon: 30:42
And so, I’m not surprised that that professor had an intuition that you could be a great professor.
Christina: 30:47
Well, he believed in me, and I still greatly appreciate him for this. So he invited me to give it a try and we did a trial, and I came and taught. The course got really high reviews. They invited me to come back again. It got even better reviews, very high.
Jon: 31:08
Wow.
Christina: 31:08
They started inviting me to teach more, and eventually I got appointed to be an adjunct professor there because of the reviews and the student feedback and even having other professors sit in and watch. They were really happy with the content that was being delivered.
Jon: 31:26
Wow.
Christina: 31:26
So I landed this position. But I will warn that this is not normal. I know that most people have to apply. As any job, you would apply for a position as an adjunct professor, or then you have the tenured professors who permanently work there too. But you would usually go through a more traditional path. As you’ve seen from our conversation, a lot of what I did is not traditional. I like to just take another route to get there, but it worked.
Jon: 31:55
Awesome. So, yeah, that definitely answers the question of how you became a professor from being a student. It’s interesting, just as a general tip, for how people could be transitioning into any kind of role. There’s lots of lessons that I could pick up from that. So I’m sure things like putting your best foot forward as a student, and then later agreeing to do alumni lectures. Probably when you were doing that, there was no obvious benefit to you. You were just going to help out. I’m sure when you did those talks, you probably prepared really well.
For the listener’s benefit. Christina was easily in at least, conservatively, the 95th percentile of being prepared for being on this podcast. So a lot of guests, the majority of guests probably, just show up and it’s like, “Well, we’re going to be talking about my experience.”
But Christina was extremely well-prepared, had great thoughts on what the questions could be on the show and even the order that they could be in. That’s truly unusual. So I wouldn’t be surprised if that same kind of preparedness has been happening throughout your career. And so, by showing up into these situations, whether it’s an alumni talk or then later doing a guest lecture, be so well-prepared and it shows. Then on top of your communication skills. So I think there’s some general tips there. I don’t know if you have other guidance or other things that you’ve learned from teaching that would be helpful for the audience to know.
Christina: 33:31
From teaching, for sure. Actually, rewinding a little bit about what you were talking about too, that I was going and doing these. I was involved in the student organizations and then the alumni talks. During this time, there was no obvious benefit for me. What was important about this time of my life, and I think it’s important for others to keep it in mind when they are starting out in data science, or whatever they’re pursuing, is that you have to be careful saying no, especially at the beginning when you’re just getting started. Maybe I have a problem of saying no. You need to say no eventually. But especially-
Jon: 34:08
You have a problem with not saying no.
Christina: 34:12
Yes, I have a problem with not saying no.
Jon: 34:12
Yeah, I hear that.
Christina: 34:13
But at the beginning it was so vital to my career because each thing that I said yes to and I got involved in, it ended up opening another door. So none of these things in the beginning … I didn’t expect later to become a professor. That actually was not in my path. I’m glad it happened, but I was doing these things just for the opportunity to help others. And I didn’t know how to say no. But at the beginning, I think it’s important to take all of these opportunities as they come. Later, as you develop your career, you need to get better at prioritizing and saying no. But don’t say the no word at the start. So that’s one little lesson.
Then you had asked as well about things that I’ve learned from teaching. I think that being a teacher has benefited my corporate career so much, because when you are a teacher, of course you’re working on your communication skills and public speaking. That’s core to the role. But then as well, when you’re teaching a subject, you realize how much you have to really understand it. You have to understand so deeply what you are going to teach to the students. So whenever I’m preparing a class or preparing content, I’m also questioning myself and thinking, “Okay, how can I explain this best to the students? I need to really understand it if I’m going to explain it.” Then on top of that, I try to foresee the questions they might ask me. So then I prepare in advance what is the answer.
Jon: 35:46
Yeah, I can tell.
Christina: 35:48
It’s like this endless loop for preparation, but it really has helped me on the corporate side, just understanding more about data science, analytics, and so on.
Jon: 36:00
Nice. I guess thinking more generally, with all of the different cultures and experiences that you’ve had, traveling, speaking, teaching, working in different countries, and it goes beyond just what we’ve explicitly talked about on the show so far, you are a frequent speaker at international conferences. Prior to the pandemic, you would’ve been physically going to these countries all over the world. Now you’re probably maybe again now traveling there and probably frequently calling in virtually into these international conferences. So what lessons have you learned from that experience, doing all of this teaching and working, traveling, speaking in so many different cultures?
Christina: 36:42
So many lessons. I love being involved with other cultures and learning about other cultures, meeting other people, and then having the opportunity to both travel for leisure, but for work as well. At least pre-pandemic, I traveled quite often with IE Business School, because not only am I a professor, but I do a side job for them, which is helping their business development teams. So I’ll go with their business development teams in different regions around the world and help them with giving master classes for potential students, speaking at corporate partners that they may have in different countries.
By doing this, I’m obviously going and working in a lot of different countries. I’ve had the chance to travel a lot through Central Asia, through Middle East. This is very different from a European way of working or an American way of working that I was more familiar with.
So I’ve learned a lot of lessons along the way, especially just being more aware of other culture. When you go to another country and you’re working there and you’re mingling with people, you have to be aware of the differences. The more aware you can be, the more successful you’re going to be.
So I take this into account before I travel to a new country and I’m going to be speaking with people there or giving a talk. I try to consider what cultural things do I need to have at the top of my mind. So to give you an example of a good lesson I learned, I realized I was not as aware as I should have been. So I was giving a talk for Saudi Arabia. After finishing the talk, I was told that one of the images I used was inappropriate, that I need to remove it for future talks. The image was just … It was like a couple sitting at a table and there was wine sitting on the table. I just hadn’t thought about it in advance. I had used this picture before in other talks. But you need to be super aware when you’re speaking in another culture.
Jon: 38:48
Yeah, you could have a sensor over it. It’s like it’s blurred out. You’re like, “What is on the table in front of them?” Well, we’ll never know.
Christina: 38:54
I have no idea. No, so this made me realize, oops, I wasn’t as aware as I should have been. There’s constantly these little things that you have to keep in mind. So awareness is just so important.
Jon: 39:11
Yeah, great story there. All right. So that was a general question. Let’s get even more general in terms of broad data science context. But more specific to something that might be of interest to our listeners, what skills do you think are most important for people working in data, particularly data science today?
Christina: 39:31
Yeah, we can start with the hard skills, but the only hard skill that I would absolutely recommend that everyone needs to learn, and probably some listeners will not be surprised by my answer, it’s SQL or SQL. I think everyone needs to know SQL or SQL. It doesn’t matter if you’re a data engineer, an ML engineer, a data scientist, a data analyst, you’re going to touch on it. Learn it. It’s easy, I think. The rest from there is the tech stack that you need to be familiar with depending on your position.
I think more important is the soft skills side. I think no matter what your position is, you should start working early on your communication skills, because these can take you far. This is going to open doors for you. Communication skills are important really right at the beginning of your career.
For you to land a job, you need to convince someone in an interview to hire you. The better your communication skills are, the easier it is for you to land these jobs. So just getting in the door to begin with. From there, having solid communication skills can help you grow within a company, can help you transition. I don’t know. I think it’s the most important skill.
Especially in tech, you have a lot, of course, introverted people that maybe don’t want to put their face out there. They don’t want to be speaking up, which means you can stand out even more. But I think that … And, also, it might be a surprise to you, but I’m pretty introverted, too. I prefer to be in the weekends with my books and not with people.
Jon: 41:12
Based on everything that’s happened in the episode so far, that probably would be surprising to a listener. But it won’t be based on what’s going to happen right after this topic.
Christina: 41:20
Okay. So, yeah, this is again pushing yourself out of your comfort zone, communicating. Then the point I wanted to get at is that with tech, a lot of times one really important skill with communication is learning how to communicate very complex topics in simple, easy to understand terms, because one of the most valuable positions is selling the data, the project that you put together to business stakeholders. So if you can be the person to do that, you have a lot more opportunity in front of you-
Jon: 41:54
Totally.
Christina: 41:55
… instead of just being in the back office.
Jon: 41:57
Right. Yup, that makes a huge amount of sense. Very practical advice. So your big skills that people in data should know are SQL and then whatever role-specific tech stack that they need to know on the hard side. Then on the soft side, emphasizing communication, which is, I think, the answer that comes up most when I ask questions like what are you looking for in people that you hire. Guests will often say communication. This makes perfect sense. You’ve explained it really well. I think it’s great to reinforce that with the audience, particularly this idea that selling … Being able to sell complex topics to a non-technical audience.
That will really accelerate your career, no question. Whether you are in a small startup selling externally or you’re in a bigger company, whether you are aware of it or not, you are selling internally all the time. What is the value of what you’re doing or what you could be doing or what your team could be doing? You’re always selling.
Christina: 43:00
That’s a good point. So communication and sales, it sounds like, is important.
Jon: 43:05
All right. So you were talking in your last answer about being introverted, which, yes, based on what an amazing communicator you are, and especially being in person with, you’re this incredibly engaging speaker. And so, I would never have guessed. Except that it does seem like there is some introversion under there because you run a group that goes under the hashtag #bookaweekchallenge. So tell us about the book a week challenge. Some of it sounds somewhat self-explanatory. We burn a book every week. Then, yeah, tell us if you’ve got some particularly big recommendations for the audience.
Christina: 43:48
Yeah, of course. This is my favorite thing to talk about. So this is my baby. This is my passion, the #bookaweekchallenge. It actually started as a personal challenge to myself that I wanted to pick up reading again. I did reading a lot as a child. I was crazy about books. I would not put my books down. Then I left that habit aside in high school, college. You have more important things to do, I guess, at that point in your life.
Jon: 44:16
You’ve got to read the books that they tell you to read.
Christina: 44:18
Oh, that’s true. You have to read your uni books. So I picked back up. This challenge started as a personal thing. Then I started to do it more publicly under #bookaweekchallenge that I host over LinkedIn. I started to do it publicly partly because there’s an element that if you do something publicly, it forces you to stick to it. You have more of a-
Jon: 44:42
100%.
Christina: 44:42
Yeah, you have more pressure.
Jon: 44:45
If there’s something that you are … Something that you would like to be doing regularly, that you haven’t been able to intrinsically sum up the focus to do regularly, I 100% agree this idea of sharing your work publicly, whether it could be a LinkedIn post or it could be a blog post, it could be running an in-person group, that idea has dramatically transformed my career, personally. The show isn’t about me, but really quickly around 2016, for a couple of years, I’d been really aware of deep learning. I wanted to be studying deep learning deeply. But weekends would go … You’re busy with work and weekends would go by. I’d be like, “Oh yeah, that chapter that I was going to read about deep learning,” I just didn’t do it again.
And so, eventually I just forced myself by creating a group, a deep learning study group, that was going to meet on a date and we were going to cover a specific chapter. And so, all of a sudden I had to do it. No matter what else was going on in my life, I had to make the time for that. That indirectly has led to everything that I’m doing since hosting a podcast, writing a book on deep learning. All of those things happened as a result of forcing myself to do it. So I couldn’t agree more that having a hashtag like this book a week challenge is a great way to force yourself to read a book a week.
Christina: 46:03
It works, it works. So I started it selfishly for that reason, to make sure I stayed on board, but then as well because I wanted to create a community effect, a community effort, and bring more people onto the challenge and try to enforce more healthy habits and continuous learning, because I’m it all into education, continuous learning.
Jon: 46:24
Another reason why doing it publicly can be so helpful. So not only do you read a book a week, but lots of people in the group, if not reading a book a week, they’re probably reading a lot more than they otherwise would be by having this community around that are doing it.
Christina: 46:39
Which reminds me that I also created #bookamonthchallenge for those that cannot stay-
Jon: 46:45
Oh, nice.
Christina: 46:46
… because I understand a book a week is a lot. So we also have #bookamonthchallenge, which I think practically anyone can finish one book a month. So that’s a little bit more broader.
Jon: 46:57
Is it like your top pick from the previous month or something?
Christina: 47:02
No, I actually post every book under the book a month challenge, even though I just use … Every book is book a weekend book a month for me.
Jon: 47:11
I see.
Christina: 47:11
But you’re right, I could do that, but I don’t. It’s hard for me to pick my favorite. Every book is valid.
Jon: 47:21
And yet we’re going to make you right now.
Christina: 47:23
Oh, yeah. Okay.
Jon: 47:25
Yeah. So actually that’d be a fun one for you. Given all the reading that you’ve done, what’s your all-time favorite book?
Christina: 47:30
I don’t know if I could pick the book, the all-time favorite, but for those that know me from this challenge or know about my reading habits, you would know that my favorite genre is sci-fi. I love science fiction. One thing about the challenge that I recommend others as well is that you actually should change the type of books that you’re reading. Don’t always stick to the same thing. So I’m constantly changing, sci-fi, tech, neuro neuroscience, physics, historical fiction. I jump all over the place. But I have to force myself to do this because if it were up to me, I would just read sci-fi every week. So my top sci-fi, there are three books that are my absolute favorite. You have Seveneves by Neal Stephenson. There is Aurora by Kim Stanley Robinson. Then Children of Time by Adrian Tchaikovsky. All of these are typical sci-fi, tech-
Jon: 48:29
Nice.
Christina: 48:30
… outer space, artificial intelligence.
Jon: 48:32
Neil Stephenson is a really famous author in this space. Cryptonomicon is a big one.
Christina: 48:37
I haven’t read that one, but I know it. I know of it.
Jon: 48:39
Yeah. I haven’t read any of it, but that’s a book that has been recommended to me a bunch of times in my life.
Christina: 48:44
Well, you can add Seveneves as well next to that because it’s very, very good.
Jon: 48:50
Nice.
Christina: 48:50
So that’s more the sci-fi. But to get it more on track with our conversation and our listeners tuning in for data and tech, I will say that these sci-fi helps my creativity, especially when it comes to thinking about the future of artificial intelligence and technology. It can help in that sense. It definitely does for me. But then if you want something a little bit more grounded, more data or tech-focused, I have a lot of suggestions. But to narrow it down, it may have been suggested before, but Weapons of Math Destruction by Cathy O’Neil is great for data ethics, to be more aware of the challenges that all of these algorithms and the data around us, the challenges that it poses for us as a society.
We’re at this moment in time that we really need to be aware of it and we need to make changes so that some of the negatives of algorithms don’t come back and hurt us. So there’s that book as well.
Another one, maybe not as well-known, but one that I found really interesting, because I wasn’t aware of this at all, it’s called Army of None. It’s about autonomous weapons. So artificial intelligence within warfare. So the author is Paul Scharre. This was just the first book that I really got a touch of data as it pertains to warfare. It’s just not something that I think about, but it’s going to become more and more important when it comes to governments and military. So that was super-
Jon: 50:29
And apparently international conflict happening all of a sudden again.
Christina: 50:35
Yeah, that’s true.
Jon: 50:36
We might have never thought in our lifetimes that that would happen.
Christina: 50:38
Yeah, yeah. It probably links a lot to the data ethics as well. There’s data ethics pieces that we need to keep in mind. So there’s that book. Then another one that I really like, especially for our beginners, it’s called Be Data Literate by Jordan Morrow.
Jon: 50:54
Oh, yeah.
Christina: 50:54
I think that’s a great book to get started for those wanting to understand the data literacy skills that you need to succeed in business.
Jon: 51:02
Yeah. Super well-known speaker, Jordan Morrow, in our space.
Christina: 51:05
Yeah, he is.
Jon: 51:07
Very cool. Well, those are awesome recommendations and I expected nothing less, given your book a week challenge and your book a month challenge, it turns out. So we had a question from Dr. Joseph Ahern that was actually about what top books you recommend. And so, that we’ve already done. I hope you enjoyed the answers there, Dr. Ahern. Then we also had a question from Ankita. So Ankita asked how it is different working at a very large analytics team like Google might have relative to a smaller analytics team.
Christina: 51:43
This is a great question. I would say that, interestingly enough, when I made that move from Google to Waze, it was like I was moving to a small company. Within Google, yes, but I was still moving to this smaller company with a smaller analytics team. So I’ve had this taste of being on a small analytics team in a smaller company. But, in general, what I would say the difference is is when you’re working in a small-medium business maybe and you’re a small analytics team, many times you have to be more scrappy, I would say, that you’ve got to do a lot more with your time. You don’t get to be maybe as specialized as an analytics team and a huge company that might have analysts and data scientists dedicated to very specific things on each team.
Whereas in a smaller company, you have this analytics team that has to do a whole lot of everything.
So you’ve got to be more scrappy. You’ve got to be more flexible to adapt. Of course, you’re learning tons of new things. I think you even probably get challenged to learn more than you do at a big company where you’ve already got a specific focus that you have to master. So you become a little bit more of a generalist. Then depending on how you use your time, you might be able to focus on a few things. But that would be the big thing that I see is being a specialist at a bigger company to having to be more of a generalist, being scrappy, and doing a whole lot more with the data that you have.
Jon: 53:08
Great answer. Crystal clear. So I hope that is helpful, Ankita, and other listeners out there. So, Christina, clearly you have a lot of brilliant thoughts. We know that we can be following you on LinkedIn, particularly the book a week challenge. Are there other social media platforms that we should be following you on?
Christina: 53:29
I’m not really active anywhere else. So my main thing is-
Jon: 53:32
It makes it easy.
Christina: 53:33
Yeah, it makes it easy. I’m trying to make my life easy. Just LinkedIn. Then you can, of course, follow #bookaweekchallenge and/or #bookamonthchallenge where you’ll be able to see all the books that I recommend, but also all of the books that everyone else that is a part of the challenge also recommends.
Jon: 53:50
Awesome. Thank you so much, Christina, for being on the show. Thank you for coming to my apartment in New York to film this episode in person. It’s been so much fun. I hope we’ll be catching up with you on air sometime again soon.
Christina: 54:02
Of course. Thank you so much for having me and I hope it was helpful for our listeners.
Jon: 54:06
No doubt.
Christina: 54:08
Thanks.
Jon: 54:14
I had a delightful day hanging out with Christina and filming her episode. I hope you enjoyed hearing her data career and broader life insights as much as I did. In the episode, Christina filled us in on how unconventional paths into a data career can be overcome by getting past the inevitable negativity of struggle, adjusting your daily habits to meet your long-term goals, and learning to thrive outside your comfort zone.
She talked about how the S2 Geometry and H3 open-source systems by Google and Uber, respectively, enable accurate geospatial data queries on the 3D service of our planet, how women and other underrepresented groups in tech can advance their careers by selling themselves and smashing modesty norms, how SQL is, in her view, the most valuable hard skill in a data career while communication, especially selling complex topics to non-technical stakeholders, is the most valuable soft skill.
We had her top three data book recommendations, namely Weapons of Math Destruction, Army of None, and Be Data Literate. If you like book recommendations like the many made by Christina in today’s episode, check out the organized tallied spreadsheet of all the book recommendations we’ve had in the 600-plus episodes of this podcast by making your way to www.superdatascience.com/books.
As always, you can get all the show notes, including the transcript for this episode, the video recording, any materials mentioned on the show, the URLs for Christina’s LinkedIn profile, as well as my own social media profiles at www.superdatascience.com/603. That’s www.superdatascience.com/603.
Thanks to my colleagues at Nebula for supporting me while I create content like this Super Data Science episode for you.
Thanks, of course, to Ivana Zibert, Mario Pombo, Serg Masis, Sylvia Ogweng, and Kirill Eremenko on the Super Data Science team for managing, editing, researching, summarizing, and producing another excellent episode for us today. Keep on rocking it out there, folks, and I’m looking forward to enjoying another round of the Super Data Science Podcast with you very soon.
