#277 Jodie Burchell from dinos to psychology to data science
On a recent podcast episode, we had the pleasure of hosting Dr. Jodie Burchell, a data science advocate at JetBrains, who shared her fascinating journey from a childhood fascination with palaeontology to leading data science teams in Europe. This journey was not linear but rather a winding road that led her through studying psychology and German in university to becoming a renowned figure in the field of data science.
Dr. Burchell started her journey with an aspiration to become a paleontologist, sparked by the classic movie, Jurassic Park. However, as she grew older, she found herself drawn more towards the humanities. This led her to university, where she initially studied history and German, before eventually transitioning to a dual degree in psychology and biology.
In discussing her career transitions, Dr. Burchell provides valuable insights into the daunting yet rewarding process of career shifts. Her experiences in academia played a significant role in shaping her understanding of the profound impact data science can have in the real world. This led her to delve deeper into the field of data science, a field that is ever-evolving, with increasing specializations and a greater emphasis on developers with computer science and engineering backgrounds.
One of the most insightful parts of the discussion was Dr. Burchell's candid sharing of her transition from academia to industry. She highlighted the challenges she faced, such as her fear of the command line and insecurities about her engineering skills. Despite these challenges, she emphasized how this journey expanded her appreciation for the work of engineers.
Another intriguing part of her journey was her transition from academia to developer advocacy. Leaving academia allowed her to establish a healthier relationship with her job. She also shared how growing older has allowed her to care less about certain things, providing her with a more satisfying work experience.
Throughout the podcast, Dr. Burchell stressed the importance of multidisciplinary skills in data science. This point was underscored by her personal journey, which saw her navigate through various fields of study and work roles before finding her niche in data science. She concluded by discussing the challenges of being a technical team lead, managing both the business demands and the team's well-being, and the importance of continuously honing one's skills.
Dr. Jodie Burchell's journey is a testament to the power of curiosity, passion, and continuous learning. Her story serves as an inspiration to anyone in data science or considering it as a career. It underscores the fact that a meandering career path can lead to extraordinary places and that embracing new challenges can lead to unexpected fulfillment and purpose.
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Jodie Burchell: 0:00
Don't second guess your background. Pretty much everything you learn will in some way be useful for you, and I have friends in data science from linguistics backgrounds, from pure engineering backgrounds, from physics backgrounds, from math backgrounds, it doesn't really matter. You can't learn everything in uni. You're going to have to learn something on the job. You just need to appreciate the foundations you have and build on them.
Hello and welcome to Developers Journey, the podcast bringing you the making of stories of successful software developers to help you on your upcoming journey. I'm your host, tim Bologna. On this episode, I receive Jodie Burchell, dr Jodie Burchell actually, she is the developer advocate in data science at JetBrains. Before that, she was a lead data scientist in Odian's Generation Advert Group, europe, after finishing her PhD in psychology and postdoc in statistics or biostatistics. Well, we'll have to see how that links to double. Well, anyway, she has worked in various data science and machine learning roles across search improvements, recommendation systems, nlp and programmatic advertising. She's also very active in communities sharing knowledge in every possible form. Thus, you may have read her thoughts or seen her on stage or in a video somewhere actually everywhere. Jodie, welcome Dev Journey.
Thank you so much. I'm absolutely delighted to be on the show.
I am as well, and we've been laughing for 20 minutes, which is great. Yes, and just for the fun. At the moment when we joined the Corp, we had a government alarm on all our telephones, a trial alarm which is blasted in our ears. That was a great way to start an interview.
It was literally me joining the call and saying it's bomb tag, like the warning day.
Exactly, and I wish I could have a warning like this to someone in the guest and say, hey, it's time to record, so really glad to have you on. But before we come to your story, I want to thank the terrific listeners who support the show. Every month you are keeping the Dev Journey lights up. If you would like to join this fine crew and help me spend more time on finding phenomenal guests than editing audio tracks, please go to our website, devjourneyinfo and click on the support me on Patreon button. Even the smallest contributions are giant steps toward a sustainable Dev Journey journey. Thank you. And now back to today's guest, judy. As you know, the show exists to help the listeners understand what your story looked like and imagine how to shape their own future. So let's go back to your beginnings. Where would you place the start if you're Dev Journey?
Yeah. So I'm going to maybe do the cliched start and say it started when I was a kid, but probably not in the way that you think. So I think when a lot of people say, like, my Dev Journey started when I was a kid, it's because they got hold of a computer and they learned how to program. It's not exactly what I mean. I actually didn't learn to program until I was in my mid-20s. What I mean is the core of what I see my work as like my whole life. My whole passion in life is being a scientist, and when I was a kid I saw a Jurassic Park and I wanted to be a paleontologist, and this sort of obsession continued for years until I realized that when you're a paleontologist you have to spend a lot of hours in the sun digging up dinosaur bones, and that didn't sound like very appealing to me. So then I thought, okay, maybe I can be an inventor or some kind of other scientist. And I had, you know, all those kids that kids have, like the microscope and the geology set and the chemistry set, like I loved all of it. And then I went to high school and I realized that well, it wasn't, when I realized that science was different to what I thought it was. It was just that it was not taught that well at my school I went to like a very small country town school in country town, australia, and you know we just didn't have the resources to really teach science in a way that was particularly engaging. It didn't help that most of the science classes focused on physics and physics is not. It doesn't interest me. I'm sorry physicists, it's just it's not interesting for me. But I was kind of drawn to the humanities as a teenager. I used to play French horn, I used to like be in the school band, I did art, I did English history. This was sort of what I studied towards the end of my high school and this sort of meant that when I got to the end of high school I didn't know what I wanted to do at uni, because I knew I wanted to go to uni. I've always enjoyed school and sort of the academic side of things, but like I wasn't stupid, I knew I wasn't going to get a job if I like just studied English literature. So I was like, okay, I need to think of it practically. So in Australia you have this ability to do what's called dual degrees, so you can study two completely unrelated degrees at the same time and you just complete them in four years instead of a three. So I enrolled in an arts degree, which is like the liberal arts degree is what they'd call it in the States, and I studied history and German. Actually, I studied German at uni. It's the thing. I live in Berlin now and I forgot all the German I learned at uni. You'll see why in a second.
I won't throw a stone at you at all.
Thank you, thanks for being kind. Yes, and even after five and a half years in Germany my in German still not that good, but anyway and I enrolled in psychology, and how I got into psychology is kind of a funny story. I was doing my final year art project and I did this very like pretentious art kind of critique kind of project and I kind of pulled in this stuff from Freudian and Jungian. Psychology was very pretentious and I was like this psychologist does, kind of cool, maybe I'll go study that. Haha, psychology is a science and that is what I fell in love with and I like I fell in love with statistics, with research methodology, with measurement, and I fell in love with it to such a big degree that the German wasn't going so well, like I was getting worse and worse marks every semester. So I was like I'm going to drop this arts degree and I actually changed to a dual psychology biology degree and so like I just did double sciences had a time in my life. I loved it and this was a story that I really love to tell everyone. Towards the end of my biology degree I was studying evolutionary biology and I had the chance to do a research project and so basically, the research project was looking at this particular reproductive behavior and it's really easy to observe in crickets or get into the boring technical details, but essentially what it boiled down to was me sitting in a stinky lab for three months watching pairs of crickets mating with this red light, and it was just like that was the point where I decided to do the PhD in psychology. Let's put it that way, not biology and not biology.
Yeah, Before you move on how did you decide to go into biology? And I mean obviously not physics you told us about it before, but any other kind of scientific branch could have done it as well. Why biology, yeah?
It was because we had to do some biology units as part of our first year's psych degree because like neuroscience and things like that. But they wanted you at my university to at least have a basic grounding in human physiology. So that was why and I really liked neuroscience as well. So, yeah, it was a pretty easy decision. And once I started the biology degree I realized like I love evolutionary theory. I love it so much, it's so elegant and yeah, genetics as well and that was actually the genetics component was super helpful, I think, for some of the stuff I did in psychology later. But yeah, we'll probably talk about it a bit later. I think as much fun as the biology degree was, I still kind of regret that I didn't pick something more practical like computer sciences or statistics with the way things turned out. But I think we'll come back to that.
Sure, let's go back to crickets. What happened after that?
No, we finished the crickets, we were post-crickets. We're post three months in the stinky lab. So basically after that I enrolled in my PhD. But at that time again, I'm quite a practical person. So I was thinking I'm not entirely sure about academia. I really love doing the work but I know it's a hard career. So I really liked clinical psychology. So I enrolled in this, like it's basically a PhD program where you have your masters in clinical psych squashed together with your PhD and you do them both together again kind of like the double degree. So I trained as a psychologist, like I was going and seeing patients for a year and a half part time. Like I was fully licensed as a psychologist. I just never practiced because I was still finishing my PhD and this was probably my first big career fork. I was getting towards the end of my PhD and I was thinking, okay, I should probably start looking for a job as a psych and start thinking about what's the next step. I was like I don't want to do it. I really liked being with the patients. I really liked helping people. It was amazing work. I feel so privileged that I got to do that work because to have the trust of people who are brave enough to go through therapy. To be able to help them is one of the most amazing things I've done in my life. But I knew I couldn't do it for my whole life. It was something I took home with me a lot. It really affected me when people had really heavy things. It was too much for me. One of my supervisors was not very happy about this, but I just decided I wouldn't practice. I was like, okay, maybe I'll give this academia thing a go after all and I went into a postdoc. I was very lucky to get the postdoc. I got it through a friend who knew the supervisor was looking for someone sort of switch courses. I went into biostatistics and public health. Basically what I was studying was care patterns for people with acute cardiac events like heart attacks, and also palliative care so people at the end of life and seeing whether there were things we could do to improve hospital care triaging things like that Amazing time, my God. I learned so much. It was probably one of my favorite times of my career in terms of the work. Like the scientific work, I felt like the work was having a really direct impact. But I couldn't hack it in academia either. It's just the published cycle is so severe, it's so hard. And again, I'm very practical. I was looking ahead and I'm like if I can't make this work, I'm screwed. You can't leave academia after a certain amount of time. There's no way you can get a job. So at the end of my postdoc it wasn't that hard a decision I really was like I can't do this, and I was so lucky that at that time, data science was becoming a thing. This was back in 2014, 2015. And so I didn't quite get what the job was, but I did have science in the title and I knew that I could basically make good money. I knew this was a thing. This was around the time when it was being called like the sexiest job of the 21st century, whatever is like proper hype time. I didn't really know what I was doing. I applied to a whole bunch of jobs that I was severely underqualified for and I was very lucky that I ended up getting a job in more of a kind of like analytics role that really played to my strengths in like statistics. I knew some basic R at that point. I had kind of played around with Python during my PhD when I was procrastinating for my thesis, but I'd never touched it again and, yeah, that was sort of where the actual data science career kicked off. But you can see it was pretty convoluted part of it. I left a couple of careers behind on the way, Like my 30s. I look back at my 20s, I look back and I'm like Jesus Christ. Maybe I could have done this in a more efficient way, but it wouldn't be fun. It wouldn't be fun and I wouldn't have the amazing cricket sex stories.
So you know, there you go Hooking you back to what you said at the beginning. You said you placed the very start at realizing you want to be a scientist. Does this data science really match the idea you had before?
It actually does, and this is something I guess I am pretty clear about when I talk to people about data science, because I think even now it's a field with a lot of misconceptions and as the field has matured, I think it's matured into a couple of different things. So back in my day, data science used to be the refuge for failed academics, like this was. 80% of us were people who were like screw this, I want to go and you know, have some work-life balance and, you know, make a bit more of a stable career choice. It's changed a lot. Actually it's changed a lot over the time I've been in the field and one of the big changes has been a lot of people coming in with developer backgrounds, so people coming in with more computer science or engineering backgrounds, and they lend their own particular perspective and it's been, I think, a natural evolution as the requirements of deploying machine learning projects has become much more complicated, like large language models are one of the most extreme examples. But you know ML Ops is now its own thing and it kind of rightfully needs to be ML engineering, especially like specialized branches where people try to take big, complex models and make them leaner and more computer-efficient. This is another specialization. So you've got this kind of new area which is more engineering focused. But you will always have, in my opinion at least, this core group who are more on the research and scientific side and I don't think you can really do kind of proper prototyping, exploration, research work in data science without that scientific mindset that you need to think critically about. okay, is this data measuring what I think it's measuring? Is there bias? Is there, you know, all these kind of basic scientific considerations? Have I really looked at this at enough angles to feel comfortable that I've gotten the true story that this data wants to tell me and that's really all it is? It's not all it is. It's hard to learn these skills but, like at the core of it, that's what it is. It's a scientific relationship with the data.
It is indeed. It is indeed At least when you are lucky enough to have this data engineering somebody doing the data engineering part with you and you can focus on that part. Early stage startup, for instance, when you have to do all and everything at the same time, you have to put some consideration aside. But at some point you really have to embrace those and take them into consideration, and that's where you probably shine.
Yes, yes, and it's been an interesting part of my journey as well. So I guess, maybe just going a little bit back into the timeline of the story, when I first started working on the technical side of things, this was really hard for me. So, if you think about it, what I had spent 11 years at uni doing was training towards the same set of skills. And even though it's hard in academia because people will really evaluate you, they'll really question you, sometimes they will insult you about whether your skills are good enough yeah, it's academia. I once had a paper of you that questioned whether I, like, should be in science at all, and I went and cried in the bathroom. A lot of crying in the bathroom in my postdoc. Wow, yes, academia, yeah, shout out to my peeps who are still there in the audience.
At least you can laugh about it now.
Yeah, yeah, it's been like 10 years since I've finished my.
PhD, so they're still behind me now.
But it was kind of funny because then I had this skill set that I'd been honing for years and I was super comfortable with that skill set and then I had to turn around and learn a new skill set which was super foreign. I will confess I was so lucky that my now husband he and I had just started dating and he is a developer and had been developed for a long time and he's very non-judgmental and he's very good at teaching. But I remember the first time I tried to read in a file in R, he told me two hours because I had the slashes the wrong way. I was like working from a book that was written for Linux systems and I was working on a window system, like again started crying. This is what happens. There's a lot of points of crying in my career. Like I was super scared about using the command line because I was convinced I was going to break the computer. Like it looks like something from a 90s Hackers movie, like if you're not familiar with it, and this was the thing at the beginning of my career that I think actually really hamstrung me a lot because I was so insecure about my engineering skills as soon as I got the slightest bit of pushback, like I got questioned by I don't know. You know, some developers can be a bit like that, like, oh, you don't know this. I'd be like, yeah, I don't know this, I'm a huge idiot, like, and I really let it get to me. I kind of wish that I'd been a bit more confident back then. But you know, how can you wish for confidence, you know? Yeah, once I got my feet under me with the engineering staff and I realized actually, like some of it is generally very difficult, some of it seems difficult but it's not that hard I decided to see how far I wanted to go down the path of learning engineering skills and I got very I mean, yeah, I would say I got lucky in my third job in industry. So I started working at what was the kind of medium size startup ended up getting acquired just before I started and I worked on a team with two guys who are like true machine learning engineers, like they can do everything from you know your research, prototyping stuff to like fully maintaining things in production, and they both really smart, really nice, and they took a lot of time to teach me things and I kind of came to the conclusion I don't want to do the engineering side. I saw enough of it and I'm like I don't like it. But it was sort of this journey of me exploring incrementally like okay, I know the scientific side, how far down the engineering path to I want to go and in the end I came to the conclusion it's not for me and I'm totally fine with that decision. And it also means that I have like a, I think, a full understanding and even more respect for the job that engineers need to do. It made it easier for me to work with the engineering staff and I'm like I'm not going to be doing it with engineers because I could explain the requirements a little bit better or I knew when I just didn't understand and I just needed to tell them what I needed to be done and just leave it all to them. So I think that is a super important thing. With any career, you'll never work alone and you should never just dismiss one of your colleagues as, like whatever, like you need to respect that there's different specializations. They take time to learn. I think something I've seen other like especially ex-academic data scientists doing is having a bit of arrogance and being like I'm smarter than everyone. I have PhD, so I'll just do this engineering thing because it's not that hard and I've seen stuff they built and it ain't good. Yes, I can.
Now, both sides of the equation are really hard and you really need to start dipping your toes, whether you're on one side or the other, onto the other side and really grasp an understanding of what's happening, and it's really eye-opening and empathy empathy building and really helps you work with people, be a bit of human. It's all encompassing.
Yeah, and look like maybe a lesson I also learned from academia is I was so wrapped up in my work as an academic. It was such an important part of my identity that when I left I was actually very lucky with my first job, got lucky with a lot of jobs, but even just really good at spotting crapping interviews. They were so chill. And so about work-life balance at this place. Like we would stop at 3pm on Friday for beers. Like it was kind of just like you would get rounded up by your manager and just like come on, we're going downstairs for beers, yeah, australia you know, can recommend.
I imagine that would be Germany, but okay.
Yeah, and Germany's also, but that's at 5pm. Come on, you've got to finish your hours for the week and then.
That's true, yeah.
But yeah, this sort of complete change of pace meant that I don't take work so personally anymore, like I'm way more chill about it and that's great. I don't move to Germany, the land of work-life balance, and it's amazing, it is, it's so good.
Did you notice an effect on how you approach things the way that you I mean the fact that you are in less pressure, you have a better life balance. Did you realize an effect on how you go at your work, the result, the outcomes, maybe?
This is a really interesting question. So I would say, I would say I'd like to think I always sort of had like patience and empathy, because I kind of had to learn that as a psychologist. But I think it just sort of let me let things go a bit easier. So it meant that I don't know if someone doesn't get something back to me like in time, and it's not that important, I just let it go. I don't care because I'm like whatever you know, it's not the end of the world, it's just my job. It is very important to me and it's very important to do my job properly and be a good employee, but I guess I don't take things personally. I don't. Maybe it's also I don't care when people question my career choices and they don't and they question my skills because I chose to do the things that make me happy. Yeah, I'm aware that definitely I have weaknesses in certain areas, but I'm fine with the choices I've taken at this stage, and maybe that's part of stepping back, because in academia I would take it all so personally and I'd feel devastated if people questioned, you know, my competence in certain fields. It's been, I think, one of the biggest gifts of walking away from academia for me this ability to have a much healthier relationship with my job.
Do you think it's really? This is a loaded question, sorry, do you? Think it's really the stepping away from academia, or having 10 or 15 years of experience later, or looking at it 15 years later.
I think it's both Like. I think if I personally had stayed in academia and look this is not something I want to say across the board, because I have friends who are still in academia and they've found a way to make it a job I think for me personally it just pushed too many buttons. It pushed buttons that I guess I wasn't even aware I had until I left. It was funny when I first left academia I was really still in this mindset. In academia, at least in the universities I was at, there was this real pressure that things had to be prestigious, you had to start publishing prestigious journals and you had to work at the right institutions, you had to know the right people. That just somehow really pushed my buttons. Maybe again, maybe nowadays it wouldn't because I'm older, but when I first left I was kind of like okay, my goal is to work at a fang, because that's transferring the mindset immediately. Then I actually started to think about what I want to do day to day at work or make me happy, and I'm like well, maybe it wouldn't be the right fit. Like, if it is the right fit later on and I happen to be lucky enough to get a job there, maybe that could be part of my career, but it's not for the same reason anymore. But definitely, getting older is amazing and you just care so much less about so many things. So, yeah, I think you're right. It's a combo of both, but with my particular personality, I'm very intense. Academia was not an amazing fit for that.
I can see why you were talking about this third job and going deep and realizing not your knowledge, not your cup of tea. But you're glad you went at it, learned it and now are able to observe the other side Later on. I'm not sure which job it is number two, three, four, five. You made a choice, an interesting choice, of going into developer advocacy for legacy and still, but this is a fork. Can you tell us more about that?
I was wondering when we were going to come to this, because this is the last career change, the final one Well, maybe the final one so far. So, yeah, developer advocacy this was not on my radar, this was not something I was working towards and I have a good friend she also works at JetBrains as a dev advocate, and we were just chatting before Christmas and she mentioned this job, my current job, and she's like hey, you should apply for it. I'm like, well, I'm not really looking and I don't think this is the right fit, because I felt like I was with sales and I was like I don't want to do sales and she's like, no, no, no, no, no, it's not sales. She explained JetBrains has a particular outlook on developer advocacy. So basically, the idea is you are trying to act as liaison between your community and the product and you can kind of do this however you like, but the main thing is, first and foremost, you are the career that you came in with. You're not an influencer, you're not a developer advocate, you are a data scientist, you are a job developer, you are this, that and the other. So that obviously was important to me because we've talked about this. My obsession in life is science, so I didn't want to leave that behind. But the other thing is you have the ability to do the job however you like, and that is super important for me. I need a very long leash and it's also important for me to be a genuine person. So part of the job is I get to help other people. It's not that I get to help them become better pie charm users, but sometimes that's part of the job. It's that I make it easier for them to get into data scientist or get over barriers they're having in data science. It's personally really important for me because of those feelings I told you I had when I started. There were a couple of people who were super cool at supporting me when I got in. One of them was a mentor at my first job in Australia. I kind of want to be that person for other people, maybe not in a one-on-one fashion, but just being able to be a voice to say, hey, my background is super non-engineering, but here I am and I'm happy and I've made a career doing this. And then the other thing is I love to learn, obviously, to say to school for a long, long time and you get a lot more opportunity to get your hands in the cool new stuff that's coming out. So, yeah, that's why I made the switch. It's not necessarily a natural continuation, I would say, of my data science career, but maybe it's kind of. It feels almost like being back in academia, but without the toxicity. Being able to work on projects that I like, being able to follow my intuition about what will be fruitful area it's. Yeah, it's a really fun job and you're never bored and it's always something new to do. Like I'm flying to London next week for a conference. I think by the time this episode comes out, I will be in San Francisco for another one and then I'll be in Lithuania two weeks after that. So that definitely keeps me busy. But, like, on top of that, it's just what I get to do at those conferences. I get to talk to people, I get to meet cool people, I get to, like help out with community events. It's a nice job.
I'm trying to piece that. I know developer advocacy from the developer standpoint, but it's the first time I hear it from a data science or non-developer role, although data science still contains a lot of development, but not, not, not not not different.
Yeah, it's a bit different.
The way I picture it is you have a lot of community work, you have a lot of working with people, but this scratching the science itch that you have is it only your own projects? And then you have on top this whole. I'm going to picture it as an overhead of stuff you do as a developer advocate and you're scratching this itch on your on your, on your personal project. You're nodding right now. Oh, oh, do you find this, this a science-y part in the rest of the job as well?
It's. It's an interesting question. So you're right that, like like all the developer advocates, it's really just your own projects. So scratching the itch of direct data science work comes through projects that I work on, but I would say for me the itch gets scratched in a couple of other ways. So just even learning about new technologies can be like intellectual work, like it is.
Yeah, and so just reading about new developments that really scratches the itch for me. And it's not just, I would say, like the pure, like, say, model architectures or, you know, learning about how to run X thing on XGPU. It's even things like and this is why large language models for me now are super exciting, apart from the fact that my background is natural language processing. It's the like the debate got into an area that is personally extremely interesting, given my psychology background. There is so much work now about the measurement of things like bias, the measurement of toxicity, the measurement of truthfulness. This is oh, this is going back to stuff I haven't touched for over 10 years. And it's also the other kind of third arm I would say is science communication. It's always something I enjoyed and it's something that I actually see as integral to science. Like, you can't write a paper if you can't communicate right. So being able to explain difficult technical concepts in a digestible way, that scratches an itch for me as well, because that, for me, is science communication. It's intellectual work, it's and it's personally very satisfying because you feel like you're going to empower someone with that.
Yeah, makes a lot of sense. Did you see yourself using more and more this psychology I'm going to put it second career that you had as it enters the large language modeling and NLP, et cetera, world.
Actually this brings us back to the conversation that I ceded earlier about regretting my second degree, kind of, but never the psychology degree. So the reason I regretted my biology degree not really. I had an absolute blast doing it. I learned lots of cool stuff. But it would have been really practical to have something like computer sciences or statistics in my undergrad when I was young, when I had the energy to learn all this, instead of having to learn it bit by bit, which is how I've had to do it over my career. But the psychology qualification I regretted it. Initially I was really beating myself up when I left even actually my postdoc, I was kind of beating myself up about it, but definitely when I left I was like shit, why did I not study physics? Why did I not study something that I see all the other data scientists study? What I've actually come to realize is the core of what I learned in my degree is the measurement of behavior, and that is such a broadly applicable skill. So all of the work that I have done in data science in my career has been in some way related to this. So I worked with language, languages of behavior. I worked with how customers interact with a website. That's behavior I worked with how these programmatic systems interact with each other. It's not human behavior, but it's still behavior. And then, of course, now we're getting into much more core psychology topics with large language models. But the whole way it's actually been really valuable, and it took me a long time to appreciate it, but it's kind of amazing what kind of skills I learned and I just took for granted. It is fun.
Coming back to topics, though, that are core to what I studied, I can appreciate that I did a very traditional general engineering degree in France and when I entered the industry I said, oh crap, I'm getting my ass served by apprentices who did 10 months of programming and they can program better than me. And it took me 15 years to really appreciate the spectrum of stuff I learned and how applicable it is in every sense of the way. But it took 15 years.
Yeah, and maybe that would be maybe some advice I'd have for any listeners that are starting their career. Don't second guess your background. Pretty much everything you learn will in some way be useful for you, and I have friends in data science, from linguistics backgrounds, from pure engineering backgrounds, from physics backgrounds, from math backgrounds, it doesn't really matter. You can't learn everything in uni. You're going to have to learn something on the job. You just need to appreciate the foundations you have and build on them.
Everything you learn will be useful, even the crickets.
Well, it is. Yeah, maybe it's not directly, it's like about a life At least one You're going to come back.
I'm sure they're going to come back.
I think like let's wait 10 years and somehow I'm going to deep. Knowledge of sexual selections in other pods will become extremely useful, let's hope not Never seen ever Max playing. Come waiting for your call.
Do you have an idea of where you want to take your career from now? Are there scratches? Each is you want to scratch and that you haven't so far.
Look, I think probably the only question mark for me is going fully back into the areas that I fell in love with in undergrad. One of the things that broke my heart a little bit when I left academia was leaving behind health sciences as a topic area. I don't think anything has ever kind of made me as happy as studying health sciences. I loved the domain in my postdoc. I loved the domain in my undergrad. I loved the crickets. It would be super cool if in some way I could find a way to work that back into my career. That said, right now I'm pretty comfortable. Who knows if there's going to be a return to pure data science roles. The question mark for me about going back to a pure data science role and look, this would be in many years I'm very comfortable. It's sort of I tried doing the team lead thing. We talked about how this is your day-to-day job before we started this recording. It wasn't my favorite thing. I would say I'm not exceptionally great at it Because, again, it's the same thing I have when I was a psychologist. I take it home with me. I don't know, maybe technical team leadership. It does sound appealing, but definitely not having, I think, the care and nurturing of your employees. It's very heavy. It takes a certain type to be able to be able to juggle the demands of the business with the well-being of your employees. It's a hard job.
It is indeed. It is indeed. Not everybody is, I don't want to say cut for it, but ready for it. It's really something you have to learn. You're not ready from the get-go to do this. You really have to learn it.
I think something people don't appreciate is you have two masters. Firstly, you've got upstream and downstream. It's also that people are complicated. You're not just going to be dealing with their work concerns, but you still need to set boundaries. I'm always very nice to my bosses. Especially after I was a boss, I was like gosh, this is so hard.
There's a fantastic article from Charity Majors called the engineering manager individual contributors of Pendulum. She really advocates for going toward management and understanding what it means to be a manager, then coming back to individual contribution enriched with this knowledge and really with the empathy of saying I know what my bosses is feeling. Now I can work with that and become a better individual contributor. And working with my boss, then at some point going back maybe and learning some more and then going back and forth and not sticking to one side or the other forever. Which?
is interesting. Yeah, Although easier said than done, I guess, because I have heard maybe you can confirm that it tends to be a reluctance to let people who have done management for too long I'm using air quotes like kind of an idea that they can't do individual contribution anymore yeah, which I don't think it's yeah.
You have to work on your profile and making the air quotes as well. On your profile while being a manager if you want to still be seen as an individual contributor. In the company I work for right now, we really explicitly created the profiles to say, when you're a first time manager, you're both and we really explain both and it's in your job description that you are both, so that you don't feel trapped into sticking to this role. And only when you start becoming a manager of a manager, it becomes a career choice to really stick to management and then probably you cannot really go back. But by that time you have acquired skills that are really unique and probably left some part of IC behind. But still, you dropped a fantastic advice already of not second guessing your careers. But there's another one I would like to ask you Go ahead, because you have a very unique decision point in your story of quitting academia and really changing careers. What would be the advice we give to somebody considering this, having really had a career on their hands and really being deeply passionate about it and facing the question should I quit and do something else, or should I change and embrace something else?
Yeah, this is such a nice question and it's actually very good timing because I'm writing a talk that I'm going to give in next month, which is not quite on this topic but it touches on it. So, I guess being aware of what you're getting into. So when you're an academic, the idea is that you go so deep into a topic that you're able to publish an original piece of research finding something new, and it has, like it's so rock solid that you are fairly sure you're contributing a piece of truth to the scientific literature. That's not what you're going to be doing in business. Basically, what you need to think about is you are hired into a company that needs to cover costs, and if it's a for-profit company, they also need to make some money. So you need to be providing some value. And something I had to learn very, very quickly is you're not going to have the same kind of time frames, which means you can't be as sure about what you're presenting. You kind of have to be sure enough or try to verify things in a different way, like with machine learning, make sure that it works on a validation set and a test set. Like maybe that's probably as solid as you're going to be able to be about what you're doing. The other is you know, if you start a project and you're given X amount of time to research and it doesn't seem to be panning out good chances, it's just going to get scrapped because business doesn't have time to be messing around on something that, like it, depends on the project, depends how important it is to get it working. But if it's just an idea you had and it's not looking promising after the amount of time they've given you, it's probably going to get chalked. So it is a much more let's say it can feel more mercenary, but it's not a bad thing either. Like, to be honest, the payoff you get is that you'll often be building products that you can see people using in the real world. Like I built part of a recommender system. I still see my recommendations coming through like six years later. That's really cool. I helped contribute to the efficiency of a search engine and, like obviously I can't see the direct results, but I know that work I did improved, that it's a trade-off and you need to be prepared for the fact that the rigor that maybe you enjoyed about being a scientist will need to be compromised. But the payoff is you get a lot more stability and well, maybe if you're a better academic than I was, you can have that stability and you'll get like a payoff with you will see things that you researched, that you found, that you built actually being used, which can't always say for academia.
Often, cannot Indeed and it's very rewarding. So you have to water your wine a little bit.
Really let the emotions go, understand business and get the reward of doing a little bit less science, maybe getting the rewards of seeing your products actually built and used and get the reap, the benefits of that.
Yeah, yeah, you have to accept that you'll still be doing science, it's just to a different level of rigor with a different goal.
Amen to that. Judy thank you so much. Where would be the best place to continue this discussion with you?
I want to say thank you as well. I had a wonderful time. So few places to contact me. I have a Twitter account. I'm never going to call it. X. I'm sorry, who knows, maybe at the time this episode is released you will have changed the name back.
I have a mastodon account. I have also a website, which I've been. Actually, it was what I started when I left academia, so that's been running since about 2015, on and off, not that. And then, of course, you can always reach out to me on LinkedIn, so you just shoot me a DM if you want to get in touch and have any questions.
I'll add all those links to the show notes If you don't have to search. Just scroll down and it will be all there. Anything else to plug in before we call it a day?
No, I think that's everything.
That was fantastic. Thank you so much.
Such an interesting roller coaster, thank you so much and thanks for letting me tell some of my favorite stories, even though they're a little salubrious.
I'm glad we talked about the creates. Yes, judy, thank you so much. Thanks, and this has been another episode of Dev's Journey and we see each other next week. Bye, thanks a lot for tuning in. I hope you have enjoyed this week's episode. If you like the show, please share, rate and review. It helps more listeners discover those stories. You can find the links to all the platforms the show appears on on our website, devjourneyinfo. Creating the show every week takes a lot of time, energy and, of course, money. Would you please help me continue bringing out those inspiring stories every week by pledging a small monthly donation? You'll find our Patreon link at devjourneyinfo. And finally, don't hesitate to reach out and tell me how this week's story is shaping your future. You can find me on Twitter and at teamathabinfoinfoinfo. Talk to you soon.