Jon: 00:00:00
This is episode number 525 with Karen Jean-Francois, analytics consultant at Cardlytics and host of the Women in Data Podcast.
Jon: 00:00:14
Welcome to the SuperDataScience podcast. My name is Jon Krohn, a chief data scientist and bestselling author on Deep Learning. Each week, we bring you inspiring people and ideas to help you build a successful career in data science. Thanks for being here today, and now let’s make the complex simple.
Jon: 00:00:43
Welcome back to the SuperDataScience podcast. Today’s guest is the positivity, radiating and fabulously engaging Karen Jean-Francois. Karen works by day at Cardlytics, a publicly listed firm that digitally bridges banks and advertisers, where she is responsible for managing banking analytics, such as engagement and return on investment. Karen is also the producer and host of Women in Data, a podcast she created to strengthen listeners’ confidence in their data careers. As a formal acknowledgment of her fabulous work for the community, Karen was recognized last year as one of the Twenty in Data and Technology by the United Kingdom’s Women in Data Organization. She obtained both bachelor’s and master’s degrees in mathematics and computing from the Université Paris Sud in Paris. And during her time as a student, Karen was French national champion in the 400-meter hurdle. In today’s episode, Karen fills us in on how to overcome imposter syndrome in the data science industry, why you might want to consider becoming a data science manager versus remaining a more specialized individual contributor, the data tools that she uses day-to-day and the productivity and prioritization techniques that enable her to juggle her day job, her thriving podcast and her world-class athletic pursuits. Today’s episode will be of interest to you if you’re keen to develop your capacity to make fulfilling career choices, as well as to hurdle over the obstacles that come up in your professional life. All right, you’re ready for this? Let’s do it.
Jon: 00:02:22
Karen, welcome to the SuperDataScience podcast. I’m so excited to have you here. Where in the world are you calling in from?
Karen: 00:02:30
Hey, thank you so much for having me. I am super excited about being here. I’ve been following your podcast for a while now. And I was thinking of maybe one day I could be on the podcast and than that came much faster than I thought. But I am joining from London and excited to be speaking with you today.
Jon: 00:02:52
Yeah. I could tell from your strong London accent. You’re originally from France, I guess. I know that you studied in Paris, but it sounds like from our conversation just before the episode that you’re not from Paris.
Karen: 00:03:11
No. I am not from Paris and I’m trying very hard not to sound too French, but this is me and this is how you get me.
Jon: 00:03:19
That’s great.
Karen: 00:03:23
I am French, but from the Caribbean. I don’t know if you know, there are some islands in the Caribbean that are French with fully French or French passport. And I come from an island called Guadaloupe, which is a few islands north of [inaudible 00:03:39]. Grew up there, left when I was 19 to study in Paris. I did my third year of university in Paris and my master’s in Paris. And then I stayed in Europe.
Jon: 00:03:52
It sounds like a wonderful place to be able to go to on holidays.
Karen: 00:03:56
Definitely.
Jon: 00:03:56
Wow. Lucky. When I go home for holidays, I just go to cold Toronto. I wish I was going to Guadaloupe and going to the beach to see my family. That sounds so much nicer.
Karen: 00:04:08
The only problem I guess, is when I go home, I have so many people I have to see that I don’t get to enjoy the island that much.
Jon: 00:04:13
Oh no, you can’t meet them on the beach?
Karen: 00:04:19
Well, my family is not big on beach, which is weird. You would think if you live, my parents live two kilometers from the beach. So you can just walk to the beach, which I do in the morning when I’m back home. But getting my mom to the beach, I would need a trophy for that.
Jon: 00:04:39
Well, so speaking of trophies and walking, one thing that I noticed about your background, which is a bit tangential to the episode, but I’m really interested in hearing about is your background is a hurdler. You were listed during your master’s degree as being the best French performer on 400-meter hurdles, and during your bachelor’s as the third best French performer on the 100-meter hurdles. Tell us a bit about that. Have you been running to the beach, that two kilometer run to the beach since you were very young? How did you get into this?
Karen: 00:05:15
Yes, it’s actually a great but embarrassing story.
Jon: 00:05:22
Okay, go ahead.
Karen: 00:05:23
We’re speaking now and it might not sound like that, but I am a complete introvert, sometimes a bit shy. And when I was a kid, I was scared of people. When I started secondary schools, my teachers told my parents that, okay, Karen, she has amazing grades, but she does not speak. I was always sitting at the front, but every time they were asking me a question, I was just replying like that. No one could hear me talking basically. And they said, you need to get her to socialize with other kids and do something that’s going to help her feel more confident and that’s how I started athletics. So when I was 10, basically my parents dumped me on the track and said, Hey, Karen, here you go, go play with other people. And I loved it. So at the beginning I was thinking, oh, maybe I would rather do basketball. I’m glad I didn’t do basketball because if you throw a ball at me, I would just run in the opposite direction. That’s how I started track, actually started doing high jump, but then there is not that much. There aren’t that many coaches for high jump on the island, a tiny island, 400,000 people, not that many people, even on hurdles, most of the time I was running by myself. So it was two of us competing to a decent level. And then it was hard to be able to get good times in, which is one of the reasons why I left and moved to Paris actually.
Jon: 00:07:00
That is very interesting. And then we’re going to talk about your productivity tips later in the episode, but how was it pursuing degrees in mathematics, including a master’s degree in mathematics, engineering, statistics and computer programming, while also training to be the best French performer at the 400 meter hurdle? Has that balance always felt kind of natural to you?
Karen: 00:07:31
Natural, no. It does feel a bit natural when you’re doing it because you don’t have a choice, you have to do it. And because I started so early, I got used to climbing around these things. So basically, if you know you have to be at school for that many hours, you have exams and all these things, but you also have to be on the track for a certain number of hours, you’re going to organize your work around that. And as you said, we’re going to talk about productivity later, but there are loads of studies that show that when you have something on the side, then you get more productive than if you don’t have any, because you do have to focus on the task at hand. And I think that was what was happening when I was studying at university. I was at university all day long, and then that 6:00, I had to be on the track from 6:00 to 8:30 and then come back home. I was in Paris living by myself. So I have to take care of dinner, take care of cleaning the flat, and also study after you’ve been running for two hours is much really very productive, but I guess that’s what it was, focusing on what I had to do at that time and making sure that I knew my priorities.
Jon: 00:08:47
Nice. So we’ll get into that more. You mentioned that you have been listening to SuperDataScience for a while. I know for example, we were talking about before recording, you were talking about how you were listening to the episode, where about a year ago now, Kirill handed over the reins of the SuperDataScience podcast that he started over to me and how the kind of attention that he built, that he just wouldn’t get around to making me enough. And you were like, you were mentioning that you were listening to that well running. I guess you get sometimes still today to go for a run and you listen to podcasts as you do that, which is actually what led to me kind of being aware of you, because both of us spoke at the Data ScienceGo Virtual Conference, which was at the end of July. And you were on the DataScience podcast or panel. So the DataScienceGo people put together this graphic of some of the speakers, it included your face actually right next to mine. And I posted it on LinkedIn and said, look at these great speakers. And then you added me on LinkedIn and you said something like, it’d be great to meet each other, have a virtual coffee or something. And when I went and looked at your profile, which was admittedly months later, I realized I didn’t respond for months to that message, but sometimes I’m overwhelmed by messages.
Karen: 00:10:13
Oh, yeah. I get that.
Jon: 00:10:16
But I had a day where I was able to kind of go back and I saw that. I looked at your profile, I was like, wow, I’ve got to get Karen on an episode. And so here we are. I’m so happy that you were interested and that you’re excited about being here. So your podcast is called Women in Data, and you’ve had some great guests on recently, Sheila Byfield who’s author of In With The Old, In With The New, which is a fun book. And it sounds like that episode is a really interesting one for people to listen to. You’ve also had people like Susanna Moan, who is the chief data officer at a big retailer in the UK called Currys. I think in general, your guests skew towards the UK. But they are great female role models for people in data related industries, data analysts, data scientists, data officers, regardless of where they live in the world. I think it’s wonderful that you do this podcast and maybe you’d like to tell us a little more about it, maybe how you got started with it, why you did it, and the impact that you’re trying to have.
Karen: 00:11:29
Sure. Happy to share that. And you’re absolutely right because I am based in the UK. The majority of my network is in the UK, which is how I get in touch with my guests. But you said it very beautifully, these women are amazing. They are inspiring. And that’s why I chose the format of the podcast because wherever you are in the world, you can listen to it, doesn’t matter the time. So if you’re not on GMT, it’s okay, you can listen later. And that’s why I thought podcasting was the best option.
Karen: 00:12:03
How I started with the podcast, another embarrassing story. I feel like my life is a line of embarrassing stories and then me trying to get out of them. I had a massive imposter syndrome at some point in my career. And that comes from many, many things. There are studies that show that 70% of people have an imposter syndrome, at least once in their life. I feel like I’ve had it my whole life, not just at least once. And working in data was a massive trigger for me because I grew up in the Caribbean, we did not have a computer at home, and working in tech was just, okay, Karen, what are you doing here? And working in data analytics and data science means that, well, we all know it, it’s a male dominated industry. And I found myself when I joined Cardlytics being the only woman in the team. And while I thought this wasn’t going to be a problem, because why would it be, we’re all humans, we all data professionals, I found that I was struggling with that quite a lot.
Karen: 00:13:16
So I struggled to really find my space. I struggled to understand if I was really fitting in that team, but in the field as well. And being able to find people who looked a bit more like me, understand that difference is where, being different is actually a strength. It’s not a weakness. It was very important for me and I found that through the Women in Data Community. So through some amazing women I met there, who helped me through mentoring, through some breakfast chats as well, telling me, Karen, you are not crazy. You are not useless. Don’t worry. You’re great. Just do what you do best. And I wanted to make sure that all these conversations I was having, all these women that I could tap into because I was brave enough to ask questions. I wanted them to be available to everybody, because in data, we are facing different challenges. We’re all different. Everybody would have a different challenge.
Karen: 00:14:21
Careers are not that well-defined in data analytics and data science. So it means, especially for people coming into the field, it can be a bit overwhelming sometimes understanding what can you do? How can you step up? Which direction can you take? So showing also different career journeys, which is what you are doing as well, is something that’s very important for me. I approached women in data in the UK about two years ago, and I told them, can we please do a podcast because you have some amazing women in the community, and I know some great women who could really bring something to others and that’s how it started really. They were crazy enough to say yes. What did I know about podcaststing? And yeah, we’ve been running the podcast for a year and a half now.
Jon: 00:15:16
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Jon: 00:15:58
I love that you’re doing that. And I can imagine the need that you meet out there. I think that given how historically and currently underrepresented, women are in data science and hopefully that’s something that will continue to change over the coming years and decades, but I could see how it right now, I haven’t personally experienced it, but I can imagine how being in a scenario where you people are just from different backgrounds that it’s tough to feel confidence that you fit in.
Karen: 00:16:38
And in fact, the fact that people are all from a different background is the beauty of data science and data analytics, because diversity is what makes a great team, but making sure that you can actually, you don’t feel out of place basically. I remember our CEO coming to the office one day to talk to the women community of Cardlytics. And she was saying, if you don’t see anyone at the company, that if you don’t see yourself in the seat of anyone else at the company, then you’re in the wrong place. And that properly sent me in a panic mode, because I was looking around me and I was thinking, first of all, I think differently, I do things differently, and I don’t look like them. So am I in the wrong company? It’s almost five years I’m at Cardlytics now and she said that a while back. I managed to go over that and understand that, okay, maybe there is no need to panic. These are things that need to be addressed. So on the podcast, really what I’m trying to do is, as I said, showcase some female role models to inspire others, but also bring more transparency on careers in data.
Jon: 00:17:58
Nice. Speaking of the imposter syndrome that you mentioned on the beginning of describing your podcast and why you got started with it, I did a Five-Minute Friday on that, Episode 502, which goes into what imposter syndrome is, how common it is. As you mentioned, I didn’t have that exact set of 70% of people experiencing it at some point in their lives, though that’s unsurprising. And so, that episode also has practical tips for overcoming imposter syndrome. And now, if I ever do that again, I’m going to have to add, start your own podcast.
Karen: 00:18:35
Exactly. Starting your own podcast is such a great way of doing these things and building confidence. Obviously not everybody really wants to start a podcast but doing something on the side is always so beneficial.
Jon: 00:18:49
Yeah. Well, obviously I agree with you. Nice. In your answer to my previous questions about your Women in Data Podcast, you did mention your company, Cardlytics. Tell us about what Cardlytics is and what you do there as an analytics consultant?
Karen: 00:19:12
Sure. What I’m going to try to do is to give you some US examples, because me being based in the UK, I am full of UK examples that might resonate with you.
Jon: 00:19:23
Well, we have lots of UK listeners too. So I appreciate that.
Karen: 00:19:28
All right. Well, we can do both. Cardlytics, what we’re doing is we are acting as a platform. It’s kind of a bridge between advertisers and banks. It can be banks or open banking platforms as well. We’re working with Nectar in the UK, for example, as an open banking platform. And what we’re doing is we’re pushing cashback offers on bank digital channels. Let’s imagine in the US if you bank with Bank of America or in the UK with Lloyds, you would go on your mobile app or on your online banking and you will get, or if you go spend that Starbucks, we will give you 5% back on your purchase and that will go directly on your bank account. That’s what we do.
Karen: 00:20:16
And my role there, although I’ve worn many hats in the company, as I mentioned, I’ve been there almost five years now, but at the moment what I’m doing is I’m responsible for everything that is bank analytics. And although my job title is an analytics consultant, I see myself more as an analytics manager because I manage all the relationship with, well, not their relationship, the analytic relationship with the banks, managing a small team, making sure that we meet all the needs of the clients. And that goes from advertising, because although the banks are where we publish offers, we can also run offers with them for their own products. So advertising, helping them understand the value of their program. If we launch a new product, what impact is it going to have on your customers, what return on investment would you have, how your customers engage with the program, do we have some customers that are more engaged than others? If some are exhibiting a behavior of customers that could potentially spend more or reading more offers, how do we get them to the next step and things like that. Also dealing with a lot of data issues sometimes.
Karen: 00:21:38
That’s my role in a nutshell, and on the side I do quite a few things as well. I do like to get involved in the day-to-day of the company and making it a great place to be, because Cardlytics in the UK, it’s about, I think it’s just maybe 80 of us now. The team has grown during lockdown as the whole company. So it’s still quite small in the UK and it just feels like a big family. Also not too big for me because my family is definitely bigger than that. So getting involved with everybody is great for me.
Jon: 00:22:18
Nice. That’s very cool to get insight into what your role as the analytics consultant title or this kind of analytics management role involves. And you got there from being a data analyst. So a senior data analyst at Cardlytics for years, and then moved into this position. And you had a series of data analyst roles before that, including at big media companies like Havas, and you worked as a statistician for ALDF in France previously as well. How did your background as someone who studied mathematics, statistics, computer programming as a bachelor’s and master’s student help you transition to being a data analyst, and then what was the journey like from being a data analyst to being the kind of analytics manager that you are now?
Karen: 00:23:22
I want to say that, I did my masters in applied statistics and mathematics engineering, but that was not my aim. I never thought I was going to be a data professional, as I said, it’s more than in the Caribbean, no one knows about data. If you ask my mom, even today, what I do, she will tell you Karen, she works in finance, which is not the case, but I’ve decided to let it go now. So basically, I was training to be a teacher because I wanted to be a math teacher. Math is something I love. My granddad was a math teacher and he was my role model growing up. And I just wanted to be like him. And I randomly stumbled across statistics, well, applied statistics, because I had statistics before, but not really applied to data and or the things during my masters. And I thought, oh my God, this is amazing. I want to do that now. It’s only during my master’s that I started considering a career in data and just went with the flow. In Paris, I was doing a lot of statistical modeling. So something that would be very close to what we call today, machine learning. And then when I moved to London, it was a bit different. It was more applied to marketing and advertising and all of these kinds of things. So while in Paris, I was working with… Do you pay with checks?
Jon: 00:24:47
With checks?
Karen: 00:24:48
Yeah.
Jon: 00:24:48
Very rarely. We have checks in the United States and in Canada. So I think people know what checks are, if that’s what you’re asking.
Karen: 00:25:01
I’ve never, ever, ever seen a check in the UK.
Jon: 00:25:09
I was there a little bit, it looks like you started living in the UK in 2014. And so I stopped living there in 2012. And I did have, you mentioned Lloyds Bank earlier. So Lloyds TSB was my bank. And I remember they sent me a checkbook at the beginning of my time in the UK, which was in 2007. That was now seven years before you got there. I don’t know if I ever used them. I didn’t know that I didn’t have them. So it wouldn’t surprise me if UK had moved beyond checks completely now. I remember even then I was impressed at how easy it was to make free transfers between bank accounts, to other people, even if they were at a different bank which, is something that in the US and Canada has only happened fairly recently.
Karen: 00:26:02
Banking in the UK is amazing. It’s absolutely mind blowing. Even paying for the train, you just go with your bank card and then tapping it, [inaudible 00:26:10] you in estimating, but your listeners are probably wondering why we’re talking about checks.
Jon: 00:26:15
Me too.
Karen: 00:26:18
That’s because when I was living in France, it was like, you couldn’t do anything without the checkbook. So if you went to pay your rent, if you went to go to the doctor, you had to have a check. And people were paying quite a lot with checks. And what I was doing there is, I was working for a company called Worldline. And basically their payment provided, like you would find with WorldPay and all these ones, and the department of the company I was working for with guaranteeing checks. So you would go to a retailer say, oh, I would like to pay with a check. And then we would have a whole process behind seeing, yes, this check is safe to be accepted by the company. And if the check was coming back unpaid, we would give them the money back. And what I was doing there was I was building all the predictive models, trying to see the probability of a check to come back with no money. So that’s what I was doing. And I moved to London and started, it felt like I had changed field completely. There’s something, again with working in data can mean so many different things and-
Jon: 00:27:30
Right. You show up on day one in London and you’re like, all right, give me all the check data. I’m going to be really good shape for analyzing all your checks.
Karen: 00:27:41
Yeah. I started with a small agency and I was doing a bit of marketing and company analytics there, company analytics I’d have as well. And then when I moved to Cardlytics, I was still doing some company analytics, but what the beauty of Cardlytics is that it links back to this first job I had in Paris where I was working with transactions. So yes, in France, I was working with checks, now I’m working with debit and credit cards transactions, but it’s still transactions. And I just love looking at where people are spending their money.
Jon: 00:28:18
Wow. You’re still [inaudible 00:28:19] finance. Your mom’s right.
Karen: 00:28:22
Yeah. Kind of. So that’s me being [inaudible 00:28:25] and then wondering, okay, well, people are spending. Don’t worry, everything is anonymized, so I don’t know who is spending, but we do see what merchants were, how much they’re spending, how often they go and all these things. So what I started doing was first of all, a bit of company analytics, but also helping merchants understand their market share, how it changes, if they are losing customers to competitor and things like that. How I moved to a management job is because, as I mentioned before, imposter syndrome. Although I have a background in mathematics and I did a lot of coding, I am not as strong technically as people who would come on the market now, or as even people who are in my team already. I don’t like spending hours and hours coding. I love coding. I wouldn’t be at 9:00 PM being, oh my God, I need to code these algorithms. I will be at 9:00 PM being, oh, am I going to do a bit of yoga or going for a run?
Karen: 00:29:37
I was feeling like, maybe do I have hours at this crossroad, which a lot of us get to wait, do I go more technical or do I go towards the leadership? And going technical for me sounded wrong because I did not get as excited as others with the technology. Although I love it, it is not what keeps me up at night. I went on the leadership side and I had to [inaudible 00:30:07], because before I was closing all the time and looking at the data, so being used to be more client-facing advising more on data questions, understanding what the question is really, because when I was working, often what was happening is, I’m saying when I was working, I’m still working. But when I was a senior analyst, what was happening is that people would come to me with a question and I would just answer the question with the data. Now what I had to learn to do is really get deep into the business problem people are trying to solve. So understanding, okay, you’re asking that, but is that really what you need? And this is something I’m still working on. It takes a bit of practice because every question is different. Every business is different. But this is what I am at the moment. And obviously people management as well.
Jon: 00:31:02
Very cool. A great answer to my question. And so today, even though you’re managing now, there probably still are some data or statistical tools or approaches that you’re still using, actually definitely you are. I don’t even need to assume because I know that even just to be explaining things to clients, that you’re leveraging the results of data models and that kind of thing. So what kinds of tools do you and your team use today?
Karen: 00:31:33
Yeah, and actually in the UK, we are a small team. So I’m definitely hands-on and still digging into the data. Tools I use on a daily basis, SQL definitely. We use Vertica and typing in the database with SQL. I code a bit with R, and I used to work with SAS quite a lot in the past, a bit less now. We also build some dashboards with Tableau and we have someone in our team that is quite good with self-serve tools with Shiny]. I wouldn’t know where to start with that.
Jon: 00:32:10
Oh yeah. That’s great. If listeners are interested in the R Shiny tool that allows you to build fully functioning websites with an R code backend, Veerle van Leemput in episode 491, talked about that in a lot of detail. But this is super cool. You’re using a lot of the big names in this industry. So SQL for pulling the data that you need out of structured databases, R for doing some analysis on these data, building some statistical models on those data, and then Tableau for visualizing the data. Actually, I’ve never used SAS, but that’s kind of, it would serve a similar function to R, right? You could use that-
Karen: 00:33:00
[crosstalk 00:33:00] Yeah. I would definitely. When I started working, everybody was just, oh, if you can’t code with SAS, then we don’t want you, which feels very weird right now because no one is [inaudible 00:33:14]. But definitely, SAS would be, I guess in business people were using SAS and at university because R was developed by researchers. So R was more prominent at university. So when I graduated from my master’s, we were doing R more than SAS. Then when I started working, it was very similar functionalities, everything you can do with R, well not everything because R was ever developing, but I could do at the time, the same things.
Jon: 00:33:48
Nice. That makes sense. R is open source, so it appeals to academics, whereas SAS is a commercial tool. And it does need to be that SAS was, when you started your career 2011, 2012 coming out of your masters, it doesn’t surprise me to hear that SAS was relatively widespread then, but I don’t hear that mentioned on the podcast too much anymore.
Karen: 00:34:18
Or anywhere.
Jon: 00:34:19
Yeah. R has filled a big niche and Python, of course, too, kinds of these open source languages have really taken over where commercial products like SAS and MATLAB used to be really big.
Karen: 00:34:32
Exactly.
Jon: 00:34:34
Cool. So obviously you are capable of doing a very large number of things in any given workweek or any given week. I suspect that sometimes the weekend is your work week as well. So you have managed well, you were a student to be a top national level hurdler. Well, also completing a bachelor’s and then a master’s in a difficult technical discipline at a top university like Universite Paris Sud. So clearly, back then you were capable of balancing a lot. Now, you’re balancing being a manager, as well as doing hands-on work as an analytics consultant at a really cool company, while simultaneously running your Women in Data Podcast and still getting runs in and some yoga in, it sounds like. I’m curious to know, you don’t need to get into this in the episode as to how fast you still are today. I suspect that your little runs are still, you’re still getting a lot of work done in those. So what I really want to know and what I suspect listeners would love to hear about is whether you have any productivity or prioritization or time managed secrets to be able to not only balance all of these different things that you focus on, but do them to such, to be able to execute them at such a high level.
Karen: 00:36:09
First, I would answer the question of how fast I am today.
Jon: 00:36:13
Yes.
Karen: 00:36:14
I have no clue. But a few years back, when was that maybe four years ago, three, four years ago, I actually managed to go to the National Championships in France and make it to the final, which was very surprising to me because I stopped competing in 2011. That was after the European Championships where I had a massive burnout. And I went back to the track when I moved to London, but I was going once or twice a week. So not the 12 hours that I was doing before. And I guess, because I was older, my muscles were developing better, maybe, I don’t know. I ended up running faster than I used to.
Jon: 00:36:59
You might know this better than me, but my understanding, I read this years and years ago, so I might be butchering something here, but the top women long distance runners, they’re often in their 30s, 40s, or maybe even older 50s, which is interesting. I think that doesn’t happen with men as much. But somehow there’s difference.
Karen: 00:37:28
Yeah. I’m not that close to long distance-
Jon: 00:37:30
[crosstalk 00:37:30] Yeah, [inaudible 00:37:31].
Karen: 00:37:32
Cardlytics did drag me into running a 10k a couple of weeks back. I cannot believe I did that, but I did. A very proud moment. I think there is this thing around, when you’re a sprinter, your muscles really develop around between 24 and early 30s. So late 20s, early 30s, and that’s why you are at your peak in terms of muscle capabilities, and then after you start getting injuries. I guess, because I was older, I already had the technique. The technique you don’t focus, you just have to sharpen it. I ended up running faster than I used to. My times are not going to be crazy for the US, but for France they were pretty great. And I did 13:58 on 100 hurdles.
Jon: 00:38:30
Wow. I can’t come in anywhere close to that even if there were no hurdles.
Karen: 00:38:37
I can not run very fast without hurdles though, so that’s [inaudible 00:38:43]. If you remove the hurdles, my brain is like, where do I go? And I don’t understand.
Jon: 00:38:45
Oh, that’s funny.
Karen: 00:38:49
So you’re right. I cannot live my life without running. Although I don’t go to the track as often, although I don’t go over hurdles, I tried last time, I can’t get to my first hurdle how I used to. So I would start running and then be like, you are too far, can you come closer? But I do run regularly because this is what makes me feel good. This is something I’ve done all my life. I do not know how to function without running. I discovered lockdown, your guide during the lockdown, before that, but really getting into it in the first lockdown. And I practice Ashtanga yoga three times-
Jon: 00:39:34
Oh, yeah. I was going to ask.
Karen: 00:39:35
Yeah, because I’m quite an active person. So it has to be hard and make me sweat, otherwise I’m not happy.
Jon: 00:39:43
I take the opposite with my yoga. I do yin yoga almost every single day. But I need that. Yin yoga is, it sounds like from your nod, you’re already knocking in, but for listeners’ sake. Yin yoga is long held poses that are passive. You could imagine like, even right now, but if you’re standing and listening to the podcast, if you just lean over to touch your toes, you kind of hang there for a minute or two minutes and you’re just trying to relax, that’s what I need now in my life because, typically in the mornings, I do CrossFit style workouts, so that’s a mix of weight lifting and cardio, and that makes my muscles also tight that in the evenings I love yin yoga. It helps me get a good sleep. It helps me recover. But I come from an active yoga posture tradition. I’m trained as a yoga instructor.
Karen: 00:40:47
Oh, wow.
Jon: 00:40:48
Yeah. Vinyasa style yoga.
Karen: 00:40:50
Oh, that’s amazing.
Jon: 00:40:51
Which is similar to Ashtanga. And I don’t know. So Ashtanga, does it kind of always have the same sequence of poses?
Karen: 00:41:01
Yes.
Jon: 00:41:02
Right. And you master that, you get it.
Karen: 00:41:07
I’ve gone all the way to the half primary. I’m still struggling with the half. It’s quite long, because I think a full primary series would last an hour and a half. So now I just go half primary wishy, maybe an hour, an hour, 10 minutes.
Jon: 00:41:25
Yes. That’s a lot of yoga and it’ll tickle your challenge, that challenge tick box you’re looking for sure.
Karen: 00:41:33
Yeah. And actually fun fact is now I’m trying to integrate a bit more of yin, so at least doing once a week because I understand the benefits of it. When I started yoga, the first time I was exposed to yoga is when I moved to the UK and I was trying to make friends, how do you make friends? You go to the gym. And I went to the gym. It was a yin class. And I hated it. I was thinking, why am I just doing nothing? I don’t understand. And now I’m really starting to get more into it because I’ve understood the benefits. It’s not just the benefits for the body. It’s also benefits for the mind. And I just said, I’m a B2B and I need to clear up the clutter in my mind quite often. How I do all of that is, as I mentioned before, having something on the side means that it forces you to focus on what you have to do. And this is something I’m still working on, because doing athletics and studying is different than working, doing podcast and exercising. Because when you’re studying you know this is what you’re going to do today, you have to listen to the teachers, they can give you the exercises, you have some content to go through and then you have the exams with the deadline. Work is a bit different. And there are a lot of things involved in a workday. I had to change a bit the way I was doing things, while before I was, I’m addicted to planning, but I changed the way I was planning. Now I tend to plan around my energy levels.
Karen: 00:43:17
I know that if you ask me to do anything on a Monday morning, it’s going to be bad because I will have very low energy. So on Monday, I use Mondays for my planning because I know I love it. It energizes me. I use Monday to catch up with my direct reports, making sure that they know what’s going on in the team in the week, what kind of support they’re going to need and when, so that gives me some energy for the rest of the week. So understanding what energizes you, what drains you is very important when you are trying to do something on this side, because if I’m low on energy and I’m trying to do something that’s going to kill my energy, first of all, I’m going to make mistakes and making mistakes in data is very easy because you need a lot of focus. And then if I do that in the morning, that’s it for today, I can’t work because I don’t have any energy. So that’s how I do it. There is also this great book called Eat That Frog, which is-
Jon: 00:44:27
[crosstalk 00:44:27] Eat That Frog.
Karen: 00:44:29
Yeah, Eat That Frog, which is great with tips on how to plan. So the idea is to really take your biggest task in the morning and do that. I don’t do it all the time because sometimes in the morning, if I don’t have the energy to take the big task, I will just push it to the afternoon. But I try to do that as much as possible.
Jon: 00:44:57
I completely understand that. The idea of eating the frog, first the analogy is quite vivid of this idea of, I guess, eating a live frog whole.
Karen: 00:45:04
I know. It’s disgusting.
Jon: 00:45:06
Yeah. It’s tricky, but if you get it done at the beginning of your day, then the rest of the day seems like a breeze. And I realize that that’s what we’re supposed to be doing. People say that. I read that so many places, but interestingly, I often almost go the other way with something, at least like my very first tasks in the morning, I like some of them to be my most routine and kind of easy. Like sitting down on my computer first, I know that the first half hour, the first hour is going to be relatively straight forward and then I’m kind of there sitting. I have some of my gears turning in my head and then I’m like, okay, I look at my to-do list. So I organize my to-do list with the most important things first and then, exactly like you say, if I see that first one there, I’m like, oh, I’m not ready for that.
Karen: 00:45:54
But enough things done.
Jon: 00:45:57
Yeah, but I’ll get it done later. So I completely understand. Those are great prioritization tips and I love the recommendation of that particular book, Eat That Frog.
Karen: 00:46:08
Yeah. But I think also building strong habits is very important because if I don’t go for my run in the morning, and don’t think I’m crazy running 10 kilometers every day, I just go in the morning, run 10 minutes. So just two kilometers. And that sets me up for the day. If I don’t do that-
Jon: 00:46:27
[crosstalk 00:46:27] Wow, two kilometers?
Karen: 00:46:29
It’s really quick. So two kilometers, you go around the-
Jon: 00:46:34
[crosstalk 00:46:34] No, I mean this is really quick. I did think you would be doing more like 10 kilometers a day.
Karen: 00:46:38
No, no, because if I do 10 kilometers, first of all, I’m a sprinter. 10 kilometers is my idea, often nightmare, and running two kilometers a day means that it gets my body moving and then I’m ready for the day. And then on weekends I do longer runs.
Jon: 00:46:59
Cool. I love those productivity tips Karen and the particular book recommendation of Eat That Frog is useful as well. So you have achieved a lot in your career already and outside of your career already, what’s the biggest challenge you’ve had in your career?
Karen: 00:47:22
I would say that the biggest challenge, and it’s a theme we’ve been touching quite a lot throughout this episode is overcoming these imposter syndrome and finding my voice, because I used to think that I did not belong in the [inaudible 00:47:40] data so much that one day, so on top of yoga and running, I love baking. And one day I said, okay, maybe data is not for me, I want to be a patisserie chef. So one day I walked into a bakery and I asked the chef and I-
Jon: 00:48:00
[crosstalk 00:48:00] That’s a French culture thing.
Karen: 00:48:04
You’ve got to love the cakes.
Jon: 00:48:06
Yeah.
Karen: 00:48:08
So I went to the bakery and I asked for the chef and I said, can I come work with you on Saturdays, see if this is a job I would like to do? So for, I think it was two or three months, I went every Saturday to the bakery and I was slicing cakes, glazing tarts, making [inaudible 00:48:28]. I got some really great tips from that. So you are laughing, but trust me.
Jon: 00:48:34
No, that’s so great. I love that you dove so deep into it. That’s great.
Karen: 00:48:37
That’s me. I can’t do anything halfway. This is how far I went. I wanted to try a different job. And then thankfully I said, with the help of the Women in Data Community, but also some people at Cardlytics. I had the help from a VP at Cardlytics who really helped me understand my strengths. And turning this around, I think it’s a great achievement. And it might not seem that way, but going from, I don’t think I belong here to fully owning my career and saying, this is what I want to do and this is how I’m going to do. So I changed my job because my job was not fit for me. So being a senior analyst was not what I wanted to do. I wanted to manage team. I wanted to develop people. Something I’m very keen on is really helping people getting to where they want to be, and this is also what I’m trying to do with the podcast and other things I’m doing on this side with mentoring.
Karen: 00:49:40
So letting go of all these obsession about sharpening my technical skills, which I’m still doing some training on, I’m still trying to learn Python and all these things. But I was so focused on that before that I was not looking at where I would be stronger. I was looking at, this thing is something where I need training on. I will focus on that and I was never playing by my strength. So letting go of, I guess the overwhelming technical hard training I was trying to get into to move more towards leadership was for me the biggest achievement I’ve had, because if you are data professional, if you’re technical, you are a data scientist, you are a data analyst, letting go of writing code constantly and doing things is very, very hard. That led for me starting the podcast and also doing more mentoring. I feel so much better in my career right now and I feel like I’m in a great place to set for my future and craft the career that I really want.
Jon: 00:50:54
That’s wonderful Karen. I can empathize with that transition from the relentless focus on technical expertise toward leadership. I went through something similar a few years ago where I had this impossibly long list of technical skill that I wanted to have. And I was trying to chip away at all of those all the time. And while I could do that, it wasn’t as natural a fit for me as embracing my capacity to be a manager or focus on commercial tasks. So I can understand that there’s this tension because you kind of feel especially early career data professional that, oh, there’s this huge realm of superpowers that I could have. And you see all these people around you with this huge tool shed of superpowers available to them and you feel this pressure. That isn’t necessarily where everyone should be focused all the time. There’s a lot of room in data careers for people to be managers and to help the people with all those technical superpowers make the most of those in a commercial setting, by developing your management skills, your commercial awareness, your capacity to interface with clients and investors and this kind of thing. So I’m proud of you. I’m glad that you feel like you got your place now.
Karen: 00:52:27
Thank you. In fact, at least you mentioned, I had the same in ticking boxes. And when you work in data and technology, this place is always going to grow because things move so fast that you are always going to end up adding things to that list. And if you end up playing a keep up game, I guess, trying to catch up with all the latest technologies or these new technical development that you have to have in your toolkit, you are never going to be able to shine. And you mentioned the people who have these amazing superpowers and these great technical skills, even them don’t have all of them. So making sure you need to know where to put your focus rather than trying to be everywhere is very important.
Jon: 00:53:14
Yeah, I imagine it makes it very easy to feel imposter syndrome. If everywhere you look, you notice the superpowers that other people have that you don’t and that’s inevitable. There’s no way anybody in a data analyst, data science career path, software engineering career path, you don’t know almost anything relative to the pool of what you could know and that all of the other data scientists around you know you know less than 1%, and that’s okay. That’s a really good message there.
Jon: 00:53:58
Another question that I have for you is one that I rarely ask guests, but it’s one of my absolute favorites and you highlighted it as one that interests you. So I’m excited to ask you. Let me frame the question first. We’re a point in history, or we’ve been at a point in history for decades that we expect to continue for decades where we have ever cheaper data storage. It’s just the cost of storing data halves every couple of years, and same kind of thing with compute. The cost per unit of compute is halving every something like 18 months. We have way more sensors collecting data than ever before. We have interconnectivity on a scale that is unbelievable and constantly becoming more and more interconnected. And we have data modeling innovations that we share with each other in archive papers and GitHub and open-source software. So all of these things together allow technological advances powered by data in particular to advance at an exponentially faster pace every year. What excites you about the future given these underlying themes?
Karen: 00:55:22
All right. People might or might not agree with me, but this is my own opinion, and I would say it. It feels like we are on a never ending sprint. So you are talking about long distance runners and how they peak and all these things. So long distance runners, they pace themselves. Sprinters, you can only sprint for that much. So we are on this never ending sprint. I don’t know if we are going to have cramps at some point or what’s going to happen, but what I feel like is things are developing so fast and is it giving us enough time to process what’s going on, what’s happening. And something that really excites me about the future is the day where we understand that all the development that we are doing today means nothing if our data is not right. So you can build the best machine learning model. If your data is biased, the output is going to be wrong. And we are going to be applying that to very important fields, such as medicines, such as education and loads of other things. Now we have self-driving cars and all these things. If the data is not right, the output is never going to be. And for me, the most exciting thing about the future is, when we are going to actually say, hold on, can we get the data right? And then go and do that. So I don’t know how you feel about that, but this is kind of my feeling. We’ve had so many stories about, obviously at the moment, we haven’t properly applied to have a gigantic disastrous impact, but if we keep going down that route, it might happen and I really don’t want that to happen.
Jon: 00:57:20
Yeah. I couldn’t agree with you more Karen on how important it is for the data quality to be very high quality. So as a quick anecdote, I was recently able to hire somebody on my team who is tasked solely with ensuring our data quality is as high as possible. This is a bit of an unglamorous position. I think a lot of people, when they get involved in data analytics, data science, they want to be working on the latest machine learning models, the latest techniques, the latest tools, but if the data quality aren’t very high quality, then user experience isn’t good, data models aren’t going to be great. So I couldn’t agree with you more, and a tip for listeners is that whether you are on the business side of data science, or you are a technical expert, spend some time rolling up your sleeves and getting into the data and cleaning it up because it’s going to improve your products. It’s going to improve your models. It’s actually probably the bulk of the work, especially with some modern kinds of machine learning techniques, like Deep Learning that can automatically extract features from our data. We don’t even need to be spending our time as a data scientist coming up with features in theory. So really we’ve got to get the data right, and that’s where you should be spending your time. So great point. Thank you for [inaudible 00:58:43] that.
Karen: 00:58:46
And I will actually add to that, because as we said before, the field is so broad that there is a space for everybody. So because we are talking so much about data science and machine learning and all these new models and all these things, people get excited about it, but there is another world out there. There are other career paths and for what we know or for what you know, you might like it even more than building models because I worked on building models myself in the past. And while it was exciting, I found something I like even more. So if you don’t try something you can’t know, and really playing by your strength is what even more important than how glamorous your job sounds.
Jon: 00:59:29
For sure. Great point. All right. One last big question for you, Karen is what keeps you up at night? Let us know about things in the past that have been challenges that you’ve overcome, and as well these data quality issues that are so important for the future, but is there anything that keeps you up at night professionally?
Karen: 00:59:58
Definitely. That touches a bit on what I just said about the career is being so diverse in data. And what I found is careers in data are not very, they’re not setting stone. So everything moves, they’re not really defined, and there is a massive lack of transparency on the different careers available in data analysis, data science, and now data engineering and everything data basically. If we take the example of when we are kids, so when we are kids, we play being a teacher, we play being a cook, an explorer. We never go, oh, let’s pretend we are data scientist. And that’s because our careers are not visible enough. Hopefully this is going to change because now everybody talks about data. So I’m hoping my grandkids, if I have any one day, will talk a weird dream about being a data professional. But at the moment, the reality is that this is not the case. No one knows about it.
Karen: 01:01:04
What really keeps me up at night is that even once you exposed to the field, you don’t know what it means. I did a masters in applied statistics and statistical modeling, and I had no clue what it meant to work as a data professional while they were preparing us for that. And it’s still the case today. When you finish studying, even if it’s not at university, even if it’s a course, you don’t know what’s expecting you in the workforce. And when you are working in the field, you don’t know what else is out there. You just know what you are doing today. And this is something I’m trying to address with the podcast, but something that really keeps me up at night is all these people who get into the field or want to transition into the field and they have no clue what their career could look like. They just have this ideal in their mind.
Karen: 01:01:59
You are talking about the list of skills. And I feel like so many people feel like that, is this list of skills that you need to tick, and it never ends. And it feels like you’re never going to be a proper data professionalized because you can’t tick all the boxes. This is wrong. I’m trying to do some work around breaking these limiting beliefs, changing the vision we have of the field, the vision we have of what it means to you be a data scientist. And I’m really hoping that one day I will be able to help people to find their value, find their niche and understand what career they want to go towards in data. And for me, that means today, building your own career is not relying solely on your job description, it’s understanding your strength. It’s understanding what you like to do and going for it. Don’t be shy. Just say what it is that you want to do in your role and get it because no manager is going to, well, unless you have an amazing manager. No manager is going to tell you, oh, here is the perfect job for you, because the perfect job for you is what you know it is, no one else will know what the perfect job for you is. So you have to build it yourself. And that’s the beauty of being in data, is that we’re still defining these things, so you can actually craft your own job.
Jon: 01:03:28
Nice. That is a beautiful message Karen. And to wrap things off, you’ve already told us about one book recommendation, Eat That Frog. Do you have any other book recommendations for us?
Karen: 01:03:43
I have one that I think is relevant to what we spoke about. I kept going on and on and on about the imposter syndrome. A book that’s amazing with the imposter syndrome, and I recommend everybody to read it or listen to it. I think maybe I wish I had listened to it more than read because it’s quite lengthy, is Secret Thoughts of Successful Women. It’s not a book for women. Very often when there is women in the title people think this is for women, it’s not for women. Everybody can get some tips from it. And what address this really is the imposter syndrome. So Valerie Young, who is the author, she spent her life searching imposter syndrome among students and professionals, and she gives you some great tips. First of all, the first part is looking into understanding your imposter syndrome, what kind of imposter you are, because there are different types of imposter, five according to her. And then she goes into giving you tips to overcome your imposter syndrome. And I feel this was a very beneficial book for me. It was a great reading and I would recommend anyone who feels out of place, who feel like they are not where they should be, or have an imposer syndrome to just go and read it.
Jon: 01:05:07
Wonderful. That’s a great tip. So you’ve had tons of great guidance for people in their careers, particularly their data careers, and just generally how to be a productive person. So how can listeners stay up to the latest on your work, your thoughts and follow you? Obviously we have the Women in Data Podcast as one option. What else do you have for us?
Karen: 01:05:33
I will say, well, our most activity will be on the podcast, but you can definitely find me on LinkedIn. I am not on any other social platform because I don’t know either how these things work. I should get better at that.
Jon: 01:05:47
Yeah, they’re a distraction.
Karen: 01:05:49
So you can definitely find me on LinkedIn, send me a message and I will do my best to reply and help. I’m also going to start blogging once I’ve managed to figure out how building a website works. So I’m on that. And what I would like my blog to be about is everything I just mentioned that keeps me up at night, helping people be the best they can be in their career in data and be happy about their career. That’s what I’m going to blog about, and you will find me once I’ve launch the website on The Thriving Analyst.
Jon: 01:06:28
Nice. Thethrivinganalyst.com?
Karen: 01:06:30
Yeah.
Jon: 01:06:31
Wonderful. All right, we’ll be sure to include all of those links in the show notes. Karen I’ve really enjoyed having you on the show today. You’re such a delight to speak to. So hopefully we can have you on the show again sometime.
Karen: 01:06:44
That would be great. Thank you so much for having me. It was fun.
Jon: 01:06:54
Wow. I absolutely loved filming this episode with Karen today, her charming French accent, her warmth, her abundant positivity, and her remarkable humility given her extraordinary accomplishments. I feel so energized about the impact we can make as data scientists, both for ourselves and the broader world. I hope that you’re feeling it too. In today’s episode, Karen fill this in on how analytics consultants work with clients to understand business needs, SQL, R, SAS and Tableau tools that she uses on a daily basis. How having a passion on the side, like hosting a podcast or being a national level athletics champion can sharpen your focus on your primary job. She talked about how awareness of what tasks energize you or drain you can help you optimize the productivity of your work week, and she talked about how awareness of your natural strengths and interests can guide you in your career journey. Say to decide whether to move into a leadership role or focus more on being a technical expert.
Jon: 01:07:56
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 Karen’s social of media profiles, as well as my own social media profiles at www.www.superdatascience.com/525. That’s triple www.www.superdatascience.com/525. If you enjoyed this episode, I’d greatly appreciate it if you left a review on your favorite podcasting app or on the SuperDataScience YouTube channel. I also encourage you to let me know your thoughts on this episode directly by adding me on LinkedIn or Twitter, and then tagging me in a post about it. Your feedback is invaluable for helping us shape future episodes of the show.
Jon: 01:08:37
Thanks to Ivana, Mario, Jaime, JP, and Kirill on this SuperDataScience team for managing and producing another incredible episode for us today. Keep on rocking it out there, folks, and I’m looking forward to enjoying another round of the SuperDataScience podcast with you very soon.
