952: How to Avoid Burnout and Get Promoted, with “The Fit Data Scientist” Penelope Lafeuille

Feeling burnt out or looking to get promoted, today's episode is packed with tips on how to thrive at work. Welcome to the SuperDataScience podcast, I'm your host, John Crone. Today's guest is Penelope LaFoy, a senior data scientist at Meta Data Solutions, who's also known as the Fit Data Scientist for her practical content on how to balance fitness, nutrition and rest with work and play to make you maximally productive without burnout and to help you land that promotion. Today's episode features all her best tips, enjoy. Penelope, welcome to the SuperDataScience podcast. It is a treat to have you on the show where you're calling it from. I'm in Sani San Diego, it's currently not any cloud outside and I'm wearing a t-shirt. Very, very nice. I have had guests from San Diego on the show before and my understanding from them is that it is the best climate in the United States all year round. So I'm jealous that you live there and especially this week because at the time of recording it is Neurips in San Diego, I should be there, I should be enjoying the warm sunshine, but I'm freezing my butt off in New York. Okay. So Penelope, the reason why you're on the show is because you wrote a LinkedIn post about the show. And so, first of all, for any listeners out there, wondering how to get on the podcast. I mean, so we get hundreds of requests for people to be on the show every month. But every once in a while, somebody makes an original post that really catches my eye and I think, hey, you know, we got to get this person on the show and Penelope did that. So I'm going to include this post in the show notes so people can check it out. But basically, you wrote, I listened to 20 plus, I've listened to 20 plus data science podcasts over the past five years. These are the three that I keep coming back to and the first one, and I'm going to assume not accidentally the first one, is the Super Data Science podcast. And yeah, you said it makes machine learning ideas feel simple. Every episode feels like a mini masterclass, top researchers and practitioners. And then you mentioned, I should also mention the other podcasts that you brought up, which are also great. So data framed by data camp was your second one. And then gradient descent by weights and biases was your third and gradient descent. I realize I'm talking way too much, but gradient descent has such a great name. There's so many machine learning podcasts out there with brilliant names. Gradient descent is one of the best. And I wish that we had a funny podcast name. But anyway, we've got a bombastic one. Yeah. So Penelope, I don't know if you, I don't know. But what compelled you to write this particular post? You do create a fair bit of content on LinkedIn and more recently Instagram. But yeah. So like, what kind of, how do you decide what you're going to create in post? So for this specific post, it was because I think that Langley lately has been a lot around sharing like cheat sheets and list of books. And we are not really sure that people are actually using those cheat sheets and reading those books. And so I wanted to create a post that was a little bit of, I'm a bit fed up of all those things that people are posting without actually listening and taking actions. So I wanted to be a little bit more personal saying, those are the three podcasts that I actually listen to. And the reason why I mentioned your podcast is that I love that it blends both the technical aspect of data science of being a data scientist as well as more an applicable aspect and a product oriented and user oriented aspect that I really love because being a data scientist is not only about coding, but it's also about coding in order to solve a particular problem for a specific person. Yeah. Cool. That is what I try to do is to try to make things applied to kind of have you thinking even in the Friday, the short Friday episodes that I do on my own. I try to have kind of like a takeaway of like, even if this is a different industry, what could you be doing in yours? What lessons can you take from what we've learned in this episode? So that's cool. I'm glad to hear that that's working out. And we were actually speaking of applicability and kind of product mindset. Before we started recording, you and I were talking about a particular episode of the show that you liked that recently came out as episode number 937 with Mark Dupuis, who is a co-founder of a company called Fabby.ai and Fabby stands for fast business intelligence. So it's a platform that makes getting business insights easy for coders and non coders alike. And yeah, you mentioned that you particularly liked that one. What did you like about it? So for this one, it's because I'm on the coding side of things, but I want to learn more about the business side of things. To know what, I mean, I'm coding, but what is my code even used for? And so having companies like that, who just build something that bridges the gap between people who are technical, but don't really know a lot about the business side and people who are more on the business side, but don't know a lot about the technical side is actually amazing. And to give a very concrete example, I recently started writing online. I would say like one or two years ago and it would not have been possible if I did not have tools such as a strategy PT and Claude now. Just because I'm French, I'm writing in English. I know what I want to write about, but having those tools that kind of helped me a little bit to move across the pressure that I had about like being perfect, knowing that I need to have everything writing down on my own, having those tools is really useful. And I'm not writing all my posts with strategy PT, I'm editing them quite a bit, but it just gives me the nudge to actually jump beyond the, I'm scared of doing it. That's the way to go. I mean, your English is perfect as far as I can tell, but I could see how, you know, it makes it easier, it makes it easy to feel confident that you're writing posts that are, you know, grammatically perfect if you have these LLMs as a tool to support you. And yeah, so speaking of which, it was specifically, so it wasn't just that you wrote a post about the podcast. I wish I had enough guest slots that I could just anytime somebody wrote about the show. I could be like, do you want to come on? But the thing that caught my eyes so that I went to your profile and your headline on LinkedIn says from burnt out data scientist to $180,000 plus and promoted while building a strong body and mind. So, and actually it continues to go on. It's a, it's a really long LinkedIn headline, but it goes on to say data science and analytics science backed productivity. And I really like all of those ideas. It seems like it's allowed you to grow a following pretty quickly. You've got a newsletter that will have a link to on substack that, you know, you provide a link to prominently at the top of your profile. And you also seem to be doing stuff on Instagram, maybe targeting a slightly, I don't know how you're kind of targeting differently. But yeah, tell us about, maybe tell us about the beginning of this. Tell us about how you were a burnt out scientist and how you turn things around. Yes. I was working in finance in New York right after my master's degree. And even if I was good at what I was doing, I think that the fact that I was burnt out came from two different aspects. The first one, I was working too much. And the second one, what I was doing was not really aligned with the direction I wanted my career to grow. And so when I was waking up every morning thinking about how do you, do I see myself in five years, there was a huge disconnect between what I was doing and what I wanted to be doing in five years. And that's when I decided to change careers and to switch from finance. I was still doing data science for finance, but doing data science, more in the life science industry, because this is the industry I'm genuinely passionate about, just like studying the human body and using this technical knowledge in order to create new drugs, develop clinical trials, and so on. That was the first point. And then the second point was about, I'm not going to be successful at being a data scientist working in the life science industry if I am not taking care of my body myself. So that's when I hired a lifestyle and fitness coach. And when I started working out pretty regularly taking care of my nutrition as well as my recovery with like sleeping, recovery practices, and so on. And that's how I was able to very clearly do 180 in both my career and my personal life at the same time. Fantastic. Were you into fitness before getting this fitness and lifestyle coach, or was that something new? So I was, but I was not doing the right things, meaning that I was also burnt out in my fitness. Right. Because outside of my job, I wanted to have something that I was looking forward to. I was working out nearly every single day and I was very clearly over trained. I was not fueling my body properly, so everything was completely out of work, to be honest. Yeah. This probably happens to a lot of the kinds of people that listen to this podcast who are not only, you know, pursuing a career in data science or AI in some way, you're also listening to a podcast about it in your free time, probably. And so you're probably the kind of person that is trying to like maximize everything all the time. And so I certainly, with my workouts, I very, very often tend to overdo it, training too many days in the week, too hard on a given day, not taking enough rest days, yeah, and just kind of having the level of intensity too high on a regular basis. And so kind of instead of fitness being something that rejuvenates me, it is often something that just worries me out even more. And then I'm kind of like, you know, on the worst days, it's like a keep feeling like I just need to nap. And it's like, you know, just sitting in my desk feels like, you know, too hard. Yeah. So what kind of, how do you strike that balance? How do you, how do you design a program that is going to make you feel rejuvenated? How do you structure that? So working with my coach, we were looking at what's my work schedule. And also what are the activities that I'm doing outside of the gym that also give me energy and I that I don't want to sacrifice. So for me, for instance, I play a lot of pickle balls of pickle ball going to play tournaments and so on. So I still want to have this background. What's that, what is that game called in French? We don't have it. It's too low brow, too American, the pickle. Yeah. That's going to be weird in French. I don't know. I saw, I actually, I recently saw, there's a, I've, I've, I've lately I've been working out at this lifetime gym in, in Central Manhattan called, it's a Penn station location and they have tons of pickle ball courts. And in fact, if you, you, it's impossible, you can't pickle ball is so popular. And there's so few nice places that you can do it in Manhattan that there's a waiting list. Like I'm already a member and I pay a crazy membership fee already, but I can't access the pickle ball courts. If I want to access them, I, I do want to access them. So I'm on a waiting list to hopefully someday get an invitation. And then if I get that, I have to pay a fee, a huge fee, hundreds of dollars, plus my membership goes even higher just to have access to these pickle ball courts. But the whole reason why I'm saying this is that the other day I saw a woman in there who was wearing a t-shirt. She was playing pickle ball and it said the pickle. So I don't know, maybe that's what French people said. But yeah, so anyway, so pickle ball, you do a lot of pickle ball. That's nice. It is pretty rejuvenating. Yeah, absolutely. And it's also a great way to meet people, which is another point about rest and recovery, which is that it's also a lot about the people that you're surrounding yourself with. Because hanging out with people who want to go out every night or every weekend, spoiler alert, there is a very high likelihood that you're not going to be able to recover and to sleep well. By all means, I do go out and I do drink, but it's all about striking that balance and surrounding yourself with people and activities that you enjoy doing together, beyond just going to the bar or going to the restaurant. And if we are doing it afterwards, after playing pickle ball. Nice, yeah. And your French soup must smoke regularly, right? No, I don't. A bit of a stereotype or something that is like, I don't know if this is true, but apparently up until like the 1980s, like the men's national football team, the soccer team would like have a smoke at like half time. I would not be surprised. So that's helpful to help us understand. So you basically, you set up a schedule, a fitness schedule that's based on work, that's based on your social schedule. It kind of sounds like that means that does that mean that you need to be in a really rigid routine or is there some flexibility as well? Both, actually, meaning that I still want to be able to go to the gym four times a week. Just because I know that it's good, not only for my physical health, if I want to build or even to just maintain the muscle that I have, but also for my mental health. I typically go right after work. I work in New York time and I'm on the west coast. So it's typically in the middle of the afternoon, which is good because there are not that many people at the gym. And so it's a good break between my professional life and my personal life. And then when I go out of the gym, my friends are also out of work and so I can go hang out with them or even go play pickleball. So it's, I know that I'm going to be working out four times per week, usually Monday, Tuesday, Thursday, Friday, but it's not super rigid. I just want to get those four times per week in. Excellent. And so how does nutrition fit into this? Because you mentioned that that is part of the change that you made as well. So in addition to kind of having this balanced fitness structure, what are your key tips for refueling for your job? Yes, I would say the main two tips are around eating enough protein. So I try to have one gram of protein per pound of body weight. Just because as I'm working out, I want my muscle to have the right amount of protein in the other to be able to grow or just like maintain them and also for recovery. And the second one is about timing my calves the right way, meaning that I don't want to have a huge glucose spike when I'm in the middle of my work session and then I just want to go nap. So what I do is like usually I have most of my calves in the forms of either like fries, potatoes, fruit, sometimes ice cream mostly around my workouts before, during and after because it's the prime time for your body to absorb those calves the right way without feeling sluggish afterwards. Got it. So ice cream before, during and after working out is the key to to being a successful data scientist. I love that. And you also, you mentioned to me when I asked you before we started recording about kind of your top tips related to avoiding burnout as a data scientist, you said the rest is the most important thing. Is there anything you want to dive into on that? Absolutely. I would say more specifically around sleep. I try to have at least eight hours of sleep, which means actually more than eight hours in bed. More or less I would say eight thirty nine just because I love reading before bed to down regulate a little bit because if you're swiping on your computer, there is no way you're going to be able to follow sleep. And it's also about the quality of the sleep that you have. It's not only about eight hours, but eight hours of quality sleep with deep sleep, REM sleep to just for your brain. And the best way to have it is also to be able to down regulates and not be stressed during the day. And for me, it comes in the forms of just going on works outside, even just a five minute work in between meetings to kind of shift my brain a little bit instead of being always in the go go go mode. And instead of swiping on Instagram, I just go on works without my phone. As I forget my keys and I'm locked outside and and so just going on works outside and helping me not only focus for my meetings afterwards on my work session, but also telling my body that you can down regulate in five minutes, which then is a good way to follow sleep faster and to avoid waking up in the middle of the night if you're too stressed during the day. All of that makes perfect sense to me, such sensible advice and hopefully a lot of our listeners are doing it or can do it. Being in bed for longer than eight hours, like that like eight and a half nine hours so that you can hopefully get actually seven and a half eight hours asleep because there is always a wake time, whether you remember it or not. But if you wear like a wop or an apple watch or whatever, a Fitbit, it's a bed, you'll see that there's chunks of time that you were awake, even if you don't remember it. So that is really important and yeah, things like reading before bed, definitely getting away from your phone, getting away from screens. Oh, I'm not as good as good at it as I should be, but I definitely get the message and hopefully someday I'll be really good at avoiding. I do, I turn off my phone before I go to bed and yeah, the nights that I have enough energy I read in bed as well is definitely my best night sleeps and yeah, of course, if you have stress during the day, that for me, it'll either like it'll kind of run through my mind as I'm trying to fall asleep at the end of the day or waking up in the middle of the night and I can't fall back asleep and it's interesting how in the middle of the night, there's stressful things that might in waking life actually seem not that bad and surmountable, somehow when I'm laying there in bed, they turn into like these big crises. Absolutely. And I'm the exact same way and that's why also right before bed, I just have a five minutes routine when I take my notebook and a pen and I just write down everything that I have in my mind. It's kind of my way of telling my brain, okay, just forget this for the next nine hours and we can get to that tomorrow morning because it's written down. You don't have to think about it and it's actually a pretty useful tip, I think. Because also the next morning, you know which tasks you need to tackle because you've already, you've already written them on a piece of paper. Great idea. I love that. I should do that. All right. So we've now talked about kind of the content that you write about how you, you know, got from being a Brent out data scientist to now having a strong body in mind and flourishing as a professional, as a professional data scientist, specifically, let's talk a bit about the data science that you do. So you're a senior data scientist at a company called Medi Data Solutions. Not metadata, but like medical data, Medi Data. So yeah, tell us about what Medi Data does and what you do as a senior data scientist there. So I've been working at Medi Data for four years now and what this company does is that it creates a software that famous scale companies can use in order to run their technical trials and to analyze the data from their technical trials. And I started working there. I was focused more specifically on immunoscient treatment, meaning using your immune cells in order to direct them towards the cancer cells and hopefully be cured of cancer. And so what I'm currently working on is designing more like the backend data science of those software that famous company companies can use versus what I started working on when I just joined was more the afterwards, meaning like the analytical side of things. Once we have the data, how can we analyze them in order to have better insights for future technical trials? So it's kind of an input, output kind of situation where I'm more focused on the input now and the software that is used in order to acquire the inputs versus at the beginning working more on the outputs. Fantastic. Is there anything that you can go into a little bit of detail on like technically like I totally understand if you can't, but you know like what maybe programming languages you use or what kinds of techniques you use regularly? Absolutely. So I'm currently working on the team that uses both R and Python, which is interesting because I do have more like a statistical slash math background. So I actually love using R. I know that it's not a very popular tool to use among data scientists. You can say it. I was doing R for a decade before I got into Python, so I totally understand. It does feel especially like, and I'm similarly, everything that I see in the world professionally personally is kind of from a statistics mindset and the way that R is set up, I realize that Python people will say things like it's not even a real programming language, but for doing statistics, for working with data, for doing plots, even still today relative to Python, there's all kinds of things that you can do well in R. So you don't have to be shy about your enthusiasm for R. Yeah, and I feel that it's specifically relevant working in the life science industry, which is a lot of like biostatistics and so on. And actually, like biostatistician are using R more than Python. But I also do love Python just because then you can actually like build more like a product around Python when R is more around the math and the statistics that you can have behind it. All right. And then kind of my last technical question for you here, or question about your career. So you group in France, as people can probably tell by your accent, and it's kind of crazy for me to see that use, it's interesting how I just have such a poor sense of, as we've been talking here this whole time, I kind of have this sense that like you're my peer and we're probably probably about the same age. But according to your LinkedIn profile, you started elementary school while I was already in university. So there is a bit of a major difference here. But yes, you did, you know, you did a high school and your first degrees in France, including a diploma in engineering, a master of science in engineering at Santra, super lec with a 4.0 GPA, congratulations. And as you said, you know, lots of mathematics courses, statistics, programming, optimization, economics, and then that led you, it seems directly into a master's at Columbia University in New York. And that degree, that master's is in something that I'm pretty sure nobody on the show has ever had this particular master's before. It's management science and engineering, management science and engineering, MSNE, tell us about that particular program. Yes. So I absolutely loved this master's degree because it was blending both the technical aspects, meaning I followed a bunch of courses around programming, machine learning, deep learning. But it was also a combined program with the business school, meaning that we had specific courses where we would use this technical knowledge and work with companies in order to solve some problems that they had, meaning like, technically, I was an employee at this company for like the three months that this project was going on, which is actually an amazing way to start building your portfolio. Because when I talk to people who reach out to me on LinkedIn, the first question is always, how do I build a strong portfolio? Well, the truth is that I already had one when I was doing my master's degree because it was in the curriculum. And so that's why it was so good because then I was networking with people who are already working in the industry, building my portfolio and applying the technical skills that I was learning within the course on very specific topics that I knew people were actually working on in real life. I like that a lot. That program sounds amazing and it looks like some of the consulting projects you did were at really well-known brands like Louis Vuitton. So that's great for your portfolio. And I'm guessing that there's kind of some flexibility in what courses you take. But you took fantastic courses for a career in data science, business analytics, machine learning, optimization, stats and simulation, stochastic modeling, and then some applied things. I'm guessing this is kind of more from the business school, like financial engineering and global capital markets, really cool balance degree. I can't believe that you did all that stuff in a year. Yeah, it was actually pretty intense, but you know, it was in the middle of COVID as well. So there were not that many opportunities to actually go out and do things. So I would not say that my lifestyle was very balanced at the time. And that was also part of the reason why I ended up being a burnt out data scientist. Well, fantastic, thank you for this tour of your career. And hopefully there are, there were lots of interesting tidbits for listeners on how they can avoid burnout and feel like they're flourishing more in their career, get promotions, earn more money, accomplish more, and probably just be happier the whole time. So thank you for all of these tips across fitness, nutrition, rest and data science careers themselves. Before I let you go, Penelope, do you have a book recommendation for us? Yes. It's my favorite book this year. It's called The Five Types of Wealth by Sahil Blum. And it essentially explained that it's not only about being wealthy in your bank account, but it's also about having money in the bank, quote unquote, on other areas of your life, your health, your mental bandwidth, your time, and the people that you're hanging out with, meaning your social wealth. I highly recommend reading it. It's super actionable. There are super nice diagrams on it, so I absolutely love it. Nice. That is a great recommendation. I hope to have time to check that out. And yes, so for people who want more advice from Penelope LaFoy on everything that we talked about in today's episode, going from being burnt out to promoted, having a really high pan career in data science or related fields, probably a lot of your advice is useful for people in any field. But it's nice to have on a data science podcast like this, somebody who specializes in being a fit data scientist specifically. So Penelope, after this episode, where are the main places that people should be following you? So for more data science career advice, I would say it's going to be linked in. And for more, I would say holistic, life side is going to be sub-stack, the fit data scientist, and on Indian is just my name, Penelope LaFoy. Fantastic. Thank you, Penelope, for writing that LinkedIn post about the show. And thank you for coming on the show and providing all this knowledge to our listeners. Hopefully we can check in with you again sometime soon. Yeah. I would love to. Thank you so much for having me. About a practical episode with Penelope LaFoy, in it she covered how working out every day without proper recovery is overtraining not stress relief. How eating one gram of protein per pound of body weight supports muscle recovery and concentrating carbs around your workout prevents energy crashes during work hours. She talked about how quality sleep requires more than eight hours in bed, reading instead of scrolling before sleep and brief walks throughout the day to help her nervous system down. And she talked about how writing down everything on your mind before bed tells your brain it can let go until morning and gives you a ready made task list when you wake up. He also heard a bit about her work at Medi Data Solutions and info on the cool masters in management science and engineering she carried out at Columbia. I hope you enjoyed the conversation to be sure not to miss any of our exciting upcoming episodes. Subscribe to this podcast if you haven't already, but most importantly, I hope you'll just keep on listening until next time, keep on rockin' it out there and I'm looking forward to enjoying another round of the Super Data Science podcast with you very soon.