 Welcome to a special live episode of the Linguistics Careercast podcast. This is a podcast that's devoted to exploring careers for linguists outside academia. I am the host, I'm Laurel Sutton, and my co-host for today is Chris Stewart who's right there on your screen. Today, our three panelists will discuss non-academic career options that combine cognitive science and linguistics in keeping with the theme of the Institute this summer, and they're going to focus on their journeys from academia to industry. Each of the panelists will speak about their experiences for about 10 minutes, and then we'll go to questions both in the Zoom chat for you people who are attending on Zoom, but also live at the Institute where the lovely and talented Hadasko Tech will moderate. The podcast is being produced by Alex Johnston from Georgetown University, who you can see there, and will be available on the podcast feed in a few weeks, as well as on our YouTube channel for Linguistics Career Launch. We are recording both the audio and the video for today for anybody who needs to know that. I'm going to turn it over to Chris now who will briefly introduce our three panelists, and then we'll get into it. Okay. Well, thank you for coming today. Today on the panel, we have Jaden Ziegler, VP of Product at Olympic Technologies, and Time Gobel, the VP of UX and Design at Milo Technologies, from Milo. Michelle Gregory, SVP Senior Vice President of Data Science at 247.ai. And I thought I would maybe start with you, Michelle, if you would like to talk for 10 minutes about your path from graduate school into your present career. Yeah. Yeah, my path may have started before that. I mean, I think most people who have backgrounds in linguistics are interested in languages, other cultures, people, and that's how you get into it. And I really went to grad school in linguistics because I thought, oh, I'll get paid to travel. I can teach English as a second language. But when I got to grad school, my advisor Dan Joravsky started the same year I did, and then I realized this whole world of research, and I was good at it, and I liked it. And, you know, we had also an Institute of Cognitive Science, so I could get a joint PhD in Cognitive Science. And I think one thing that I enjoyed about my graduate career is I took linguistic theories and principles, and I used Layfeld's model of speech production, how we produce speech in the brain, what kind of information we have to hold, and did some cognitive science experiments, right? So, and learned that every time we say the same word in a conversation, they get shorter and shorter. And from that, I was able to mathematically model that data and improve speech recognition and speech synthesis, right, by burying the pitch and burying the length of words. So I had like this three parts, you know, I wasn't just computational linguistics, not just linguistics and not just cognitive science, it was very much an interdisciplinary field. And, you know, I think Dan Joravsky and I also started this field of computational phonology and doing experiments there, you know, really focusing on word durations, word frequencies and counting things, right? And so, you know, I didn't know at the time, because who does? That was a groundbreaking field that we were starting something new. I also didn't know that 20 years later, everyone would be an expert in my field after chat GPT. But from there, I did actually go into academia. I spent two years as a postdoc at Brown University, then I had a great tenure track position at SUNY Buffalo. I was offered a visiting scientist position at a national lab, which I had never heard of before, came for the summer, and then a year later decided to join permanently. And it was interesting, it's sort of like a soft money environment where you have to bring in your own work. So to do the research I wanted, I had, I needed to have clients. And from that, I learned I had the skill of translating needs, what clients need and the information they want into a mathematical scientific solution and actually could talk about it, right? And I really believed in the science and it could help solve these problems. So I was able to build a very large program there and I was there nine years, focusing mostly on social media analytics and analyzing that data. And then I was headhunted for a position at Elsevier, which many of you may know as the Books and Journal Company, but they also, that's only half their business. The other half are database products for in silico drug discovery for the pharmaceutical industry for doctors and nurses in the hospital. And I was brought in to change how they process content because it's a 130 year old company and they did this by hand. They hired a lot of outsourcing in India and they didn't trust the AI solutions and machine learning solutions we had. So it was really a lot of, it was a change going into corporate environment. And I also moved my family at the time to the Netherlands for this role. But it was really a, you know, a change management exercise and how do you measure, you know, they kept telling me for this particular product, they were getting 98% accuracy. And when I did CAPA statistics, you know, they were nowhere near. And so it turned out the automated solutions actually did work better than we were doing. And that's the kind of work I brought in there. So for all of our verticals in the business, being able to just demonstrate the impact of data science machine learning, you know, on the end product and what you want the customers to use. And my focus in my career has definitely been all about the impact, getting things shown and used. And I think, you know, data science and AI is big for companies. And I think a lot of CEOs have thought, yeah, we have a team that does that they're really smart, but I don't know what they do, right? And my magic power, if you will, is to bring that to the light, give it a lot more transparency, include the data scientists and the agile process. And along with engineering and product leads. And, and that's, you know, really my journey in a nutshell, how I got to where I did, if anyone, you know, has any more questions you can ask. So Michelle, I have a question, and this is going to be for everybody. When you were in graduate school, and even when you were doing your postdoc, were you committed to staying in academia? Or did you think that you were going to get a job in industry? Was there like an inciting event that made you think, you know what, maybe I really need to get a job that's not in a university? No, it wasn't that at all. It was actually more for, for personal reasons. I really enjoyed doing research. I was on the East Coast and a new mother and definitely wanted to be closer to my family on the West Coast, where I was from. But yeah, I had to think long and hard because I had a great tenure track position, but I have to admit, I've never looked back. I mean, what I loved about moving from academia to, and I'm sure the other panelists will find this too, to industries, you get to work on a whole broad set of skills. So I do NLP, machine learning, information retrieval, all of that. But at the same time, when I moved to the lab, I could apply that to cybersecurity, counterterrorism, law enforcement, all these different domains with the same skills. And I have hired many PhDs from academia to work in my teams. And they all say the same thing, you get a grant, you have a system that's running, seven people use it. They were really excited to get their work out and used and in the hands of people. And these are semantic technologies, everything that falls within the wheelhouse of information extraction and NLP. And I think that was the biggest difference for me. In academia, you focus on one small problem. In industry, you get to broaden that. And throughout my career, I have to admit the only thing I've seen change as a data scientist is, I wrote a paper on conditional random fields 20 years ago, and now that's one of the biggest deep learning algorithms. The algorithms haven't changed. What's changed is the computing power. And I think that is what led me into industry because you could actually not just play with these toy problems and these toy data sets, it's actually really getting that into production and used by people. So that computing power sort of enabled that throughout my career to really have the impact that as scientists, we want to see. Yeah, that's great. Thank you. That's a great answer. And just for the people who haven't listened to other episodes of the podcast, this is something that we've heard from a lot of other linguists, too, that one of the big rewards about working in industry is really seeing your linguistics in action. It's doing something. It's having an effect. It's making people's lives better or doing whatever it is, and there's a real reward that comes out of that. Yeah. Awesome. Thank you. Great. Jaden, would you like to go next? Sure. Hi, everyone. By the way, it's Pride weekend in San Francisco, so happy Pride to everyone. Happy Pride. Are you in San Francisco? I am. Yes, I am. So I'm in here doing the podcast instead of enjoying the festivities, but I'd rather be here anyway. Don't worry about it. Well, thank you all for having me. Actually, I think a lot of things that I'm going to say probably will sound similar in ways to what Michelle said, and I resonated with a lot of her story. Yeah. So I think I'm earlier on in my career. I definitely had a lot less experience overall, but I found my way into a position that I'm ultimately happy in, that I couldn't have anticipated coming into it, and I've also not looked back since, and I think I resonated with that as well. So just to kind of way of introduction, I do product management right now, which is something that I didn't even know what it was before I came into it. And so basically, in a nutshell, what product management is, is you're kind of like the driver of creating new technologies for people, right? So if I want to come up with a new app or something, I'll be the one who goes out and does the research to figure out what the pain point is that users might have that we could solve, do some research into what the solutions, possible solution sets are. Once you have a couple of solution sets, look into whether that actually solves the problem or not, and then ultimately work with design to design a solution, work with engineering to actually create the solution, see it through to completion, work with marketing to figure out how you talk about it, et cetera. So it's kind of orchestrating the entire creation of a product from beginning to end. And so that's something I didn't really know much about coming into this, and it's certainly something I stumbled into here, and I'll get into that in a second. So when I went into grad school, I actually took a very winding path. I think that'll probably resonate with a lot of folks too. So I started off originally going into Linguist Six at NYU, starting a PhD program there, working with Lena Polkinin, and then very quickly realized that some of the core questions that I was interested in weren't particularly best suited to the sets of labs that I found myself in at the time. And so ultimately, I realized that I was really interested in acquisition related questions. So I definitely have always taken the sort of cognitive science approach to language, but there was no one in the department at the time during acquisition, and the department's built out a beautiful acquisition program now. And so I ended up actually, after my second year, jumping out of the program and switching into a psychology program at Harvard, working with Jesse Snedeker to work more directly on acquisition. And so then I did my PhD very much straddling psychology and linguistics, and I'm actually very grateful that I had started that PhD in linguistics, because I think I took a lot of the core theory classes that would have missed otherwise. And so I think that really set me up for a solid foundation of being able to do probably the best research I could have done. And so I really enjoyed grad school. I had a wonderful time. Most of my research was on sort of the psycholinguistics of our human structure. Happy to talk about that at length as a side conversation. But so I was, you know, I would say that I was pretty similar to Michelle as pretty straight as an arrow kind of set on academia the entire way through, even up until my fifth year. And then in my fifth year, I kind of, I soft went on the dog market, as they say, right? So like you kind of go through the motion of putting your materials together for the job market as a way to just get the experience of doing it. Not really fully expecting that I was going to get an academic job. I had a postdoc lined up. So I was, you know, pretty, pretty set on academia. And then as I kind of got deeper into the application process and deeper into kind of thinking about what my path might look like, right? As I was trying to get toward getting that tenure track job and tenure, I started to, you know, look at the odds. And I think, you know, it's not a surprise to anyone, right? But the academic job market is pretty challenging. They're seemingly fewer and fewer jobs for more and more qualified candidates. And ultimately, you know, at the end of the day, you might do several rounds of postdocs. And then maybe you end up at a tenure track position in somewhere that you ultimately aren't happy being geographically. And so I started to do some soul searching and realizing that I did, you know, that there are things outside of me that aren't just my work, right? So, you know, there's my work self. And I think for the longest time I was sort of identifying as my work that was like who I was. But I quickly started or slowly rather started to realize that there are things outside of that that were equally, if not more important for quality of life. And so I, you know, started to care a lot more about ultimately where I ended up and would live long term. And those are just variables that you don't have a lot of control of when you're in the academic path. And so ultimately, I decided midway through 50-year grad school to try something different and give, you know, the tech industry to try to see what it was like out there and ultimately to get a little bit more of that agency in my life that I was looking for. And so for me, it felt very much last minute and like I, you know, didn't have a lot of time to figure things out. Which I think given sort of the academic-minded mindset, right, it takes a year to get a job in academia when you're applying for tenure-track jobs, you know, it kind of felt like, oh, you know, am I too late to the game, right? Do I have enough time to even find something? I had like six months before I graduated. Am I going to find something that I like, or am I going to find something period, let alone something that I like? And so once I decided to make that decision, I very quickly started having informational conversations with folks and just figuring out what even the possibilities were. So mind you, I was, you know, I was not planning to go into industry. I didn't know the vocabulary, I didn't know what kinds of jobs were available. All I knew was the network of folks that I'd had who had gone into industry prior to me. And so I tapped a lot of them and, you know, ultimately was really kind of targeting the same sets of jobs that I saw over and over again. And in my particular network, a lot of people went into data science or UX research. And I think even at the time, you know, certainly, certainly like a lot of the like linguists, more linguistics focused jobs have sprung up, especially with, you know, chat GB team and other AI advancements. But, but so anyway, sorry to digress that was kind of my experience at the time that those are the two sort of job roles that kept popping up. And I was like, ah, you know, I sort of have the skill sets for those. I might as well start looking at them. And then I quickly started, I quickly realized that it's very much a numbers game when you're applying to jobs. So for, for one, you know, it's very easy to apply to jobs in, in industry versus academia, right, rather than having to create this entire pack packet and your statement and everything. It's literally just your, you know, one page resume, you fill out a couple of text forms and you send it off. And so it's super low lift. And you can send your resume to a lot of places. You know, the sort of converse of that, right, is that like by virtue of casting a wide net, you're going to get a lot of rejections. And so you have to, you have to very quickly get a thick skin for not hearing back or getting, you know, negative news. And as I, you know, now that I'm on the other side, it's a lot easier to contextualize that, right? So a lot of the times when we're getting those auto rejects, they've already got a candidate in mind, or, you know, they've decided to take the listing off or they've, they've decided that they're looking for something very different, right. And so at the time, you know, I think this first couple were taken pretty personally, but over, over the time that I was ultimately looking for a job, I started to realize that, you know, you kind of just have to shrug it off and just keep going. And so, you know, I kind of started, as I mentioned, started looking at just kind of data sciences, user experience research jobs, and very quickly exhausted those, you know, and had, and started to realize that like, again, I was just going to have to cast a wider net than that. And so I ultimately just started looking at anything with a keyword that sounded even remotely interesting. And ultimately, I ended up at a research project manager job at Apple. And the reason that they were looking for someone with a PhD, which is how I kind of got lucky, I suppose, is that it was a sort of like internal, almost academic style run lab for folks working on AI technologies. And they wanted someone with the PhD to help manage the projects who knew something about how the research process worked. And so I really lucked out right place, right time, which I think is ultimately the through point and really any job search. And so that was kind of my way in. But again, so I, you know, I still wasn't sure what I was doing. I very quickly had to learn on the job, which, you know, by the way, you learn very quickly on the job. I think anyone with a PhD or a master's or, you know, anyone with a kind of analytical, deep, critical thinking skills, kind of training that comes out of these kinds of programs is going to very easily be able to adapt. And so, by the way, this was in 2019. And so this was six months before the pandemic hit that I found this job. I think it was very fortunate. I moved across the country, started a new life. It was, it was great to be able to go in person and, you know, meet people and really kind of learn how things worked. And then six months into the job, the pandemic hit. And that was, you know, obviously very challenging for a lot of us. I think at the time it ended up being a little bit fortuitous for me because within my particular larger organization that I was in, there, there was a product at the time translation who ultimately needed someone to step up and kind of help with the project and product managing a lot of the work there leading up to a launch. And I had kind of already started, you know, working with the folks on some small-scale analysis projects. And so it very much felt like a good fit. And also it kind of, you know, conceptually felt like a good fit, you know, machine translation. It was the closest thing to language that I thought I could kind of get to at the time. And, you know, while, you know, I know he's very different from the kind of research I was doing, it was still, it was just wildly exciting both to be able to work on slightly familiar topics, but even more so, it was wildly exciting to learn about the entire world of product management. So here where we were kind of, you know, six months before launching a new product and, you know, we were still building it, right? And so we ultimately ended up having to finish it. I very quickly had to realize or learn the entire software development process to figure out exactly kind of what boxes needed to be checked and when and how you even validate that things are running correctly and how you position your servers around the world to make sure that the data is flowing correctly and quickly. And especially at a place like Apple, right, the kind of the bar is really high for user experience. And so it was a trial by fire. I had to deal with legal privacy, you know, obviously a lot of design. And so I think, again, it was kind of fortuitous because it was this kind of job that I didn't really know much about. I kind of backed my way into it because I started as, you know, doing kind of project management for research projects and then ultimately was ending up working on a product that was going to go live. And so it was a wild time. It was, you know, very exciting to be put into that environment and to be able to learn very quickly all of the skills that I would need to be successful in that kind of job. And then it stuck, right? So I think, you know, we launched that product that continued to work with the translation team. Ultimately, we ended up launching another product, another couple of products then next year. And for a while, you know, it was wildly exciting. And I think partly because I was learning a ton at the time. And, you know, there were times where I had my doubts, right? So like here I was kind of working in a sort of what I saw as a less technical kind of role. And so I started having these doubts of like, you know, just product management, what I really want to double down on, you know, do I rather kind of prefer to jump back into something researchy or data sciencey, right, to like be able to use my technical skills. And I had to do a lot of soul searching at the time and ultimately realized that, you know, some of those insecurities were a little bit vanity of me wanting to make sure that people knew I had technical skills, right? But at the end of the day, I think what matters is that you have fun doing what you do. And what I really enjoyed about product management and why I ended up doubling down on it is just the dynamism, right? So as I mentioned, you know, there's, in any given day, I'm doing a million different things. I'm, you know, I'm design, I'm legal, I'm whatever, right? And with a technical background, you can dive more deeply, right? So like I can pull the logs for, you know, our users and like actually do some analysis on, you know, whether we're seeing the kinds of things that we would expect to see. You know, I could kind of like dive deeply into any of the corners that I care about on any, you know, any given day or as needed. And I find that to be extremely rewarding because for me, I think I'm someone who gets bored kind of easily. And so it's nice to be able to just like jump around to different things from time to time. But then it's also nice to be able to just really dive deeply into something whenever you need to and make sure that you solve it. And so I was, sorry, I'm very long winded right now. So I'll get to the point. And so then, so I decided to double down on product management. Ultimately, I jumped over to Google for a very short stint. I kind of was curious to see whether, you know, how a different large company worked and operated and whether there was, you know, anything kind of special sauce to that. As you know, now that that was my second kind of big company, what I ultimately actually ended up realizing for myself is that the kind of dynamism that I look for and the excitement that I look for is something that like would really be well suited to a startup. And so I had kind of come to the realization over time that my next thing would be a startup after Google. And it ended up coming sooner than I expected. So five months into my Google job, I ended up being poached at a startup for some, through some folks that I know. And so then I jumped over to them. It was a relatively early stage, you know, we've, we've got like 15, 20 people max. And so they ultimately needed someone to lead their entire product initiative. And so I was able to kind of step into that, that type of a role where I've been for the last year, it's been extremely amazing and rewarding and also very challenging. And especially in this environment, being at a startup is really difficult. But I think it's leveled me up in a lot of ways that I couldn't have even imagined. And yeah, and I think, you know, it's, it's been really fun to have a little bit more, even more of that kind of agency autonomy that I was kind of mentioning at the beginning that kind of drove my reason to go into industry in the first place. So, and I mean, I'm happy to go at length in questions about this. But, you know, there are a lot of differences between big tech companies and small tech companies. And I think one of the things that I learned very quickly at the start of this, that you kind of, you know, you wear all the hats, right? So you do what needs to be done when it needs to be done. And there's a beauty about that in the sense that you get to learn, you know, well, you get a lot more beyond the kind of job description that you have. And I think it was really important for me, though, to have gone through those larger companies first, because I think you really do learn a really solid foundation for what it does, what it means to do the thing that you're, you know, your job description very well. And without that, I wouldn't have been positioned to be as successful as I've been here. Yeah. And so I think kind of, to summarize that a little bit, it's, you know, I'm not, I'm maybe an interesting case where the work that I'm doing now is not particularly related to the work that I did in grad school, at least not in terms of the kind of, you know, the content of the work. But I definitely think it touches it very tendentially in a lot of ways. And certainly the skills that I learned in the PhD program, the soft skills and the skills like, you know, beyond just the core content that I was working in have come in handy time and time again in industry. And I'm happy to go into more detail on that too in the question and answers. And I'll stop there. Thanks. That was great. I wanted to pull out just a couple things before we go to Ann. One is you and Michelle both talked about the fact that you've gone from a thing to thing. And this is something that's, I think, really important for the students to hear is that unlike the academic model where you tend to go for a tenure-track job and then you work at the same university for the rest of your life, that's not what industry is about. People say job hopping makes it sound bad. That's not true at all. Moving from job to job is very important. You go to a new job, you learn things like you were saying, Jaden, you level up or you get a new skill set or it's a better fit for your personal life like Michelle was talking about moving to be closer to her family. So changing jobs is normal. Changing jobs is good. And people shouldn't think because you've changed a job that you've failed at your first job because that's not what it means at all. The other thing I wanted to just- You're using those skills in the analytic part no matter what. And I think a lot of people feel like if they're not in tenure-track positions, they're wasting their PhD, which is unfortunate. Yeah, totally. That's great. Thank you for mentioning that. I just wanted to clarify one thing real quickly. You had mentioned informational interviews. So again, for the students who may not be familiar with that term, it doesn't mean someone's interviewing you. It means you are essentially interviewing a person who has a job that you're interested in. And we use the word interview. It's not an interview. Sometimes it's just going for a cup of coffee. Sometimes it's getting on the phone or on a Zoom for 15, 20 minutes. And this person will tell you like Jaden did, like Michelle did, like Anne will do, like this is what I do. This is my job. This is how I do it. Here's what you would like to know if you were going to go into it. Maybe you can give them some advice. And networking and informational interviews are the way you get jobs. Like you really can't find a job unless you put yourself out there. Looking for a job is a job. And these are very important skills that you need to have. So I just wanted to mention that people are often confused by the term informational interviews. So Alex has just put something into the chat, which is a guide that has best practices for how to do them. And real quick, I will also say it's scary to ask people for informational interviews sometimes because it feels like you're asking for a huge favor, but almost every single time, people are happy to talk about their jobs. You know, it's paying it back to people who are coming after you. So you should never be afraid to ask. The worst thing is that somebody will say, I'm busy, can't do it this week, maybe in a month when I finished my project, people aren't going to be mad at you or feel like you're imposing on them if you're asking for an informational interview. So don't be scared about that. Yeah, I'd like to add a couple of things to that. Well, go ahead. Let's let Anne talk and then we'll get into the discussion. Sorry. Yeah, go ahead. Anne, if you like, say the best for last. No, stop. Aren't you going to say my name? You know you wanted to. Anne. I've been saying Chris, your linguist, you have to say my last name. It's to me a gobble if you really want to be picky about it, but usually it's time gobble around here because that's what people say. Okay, I don't really care anymore. But yeah, it's funny. I was already also to start talking about the topic we just, when Michelle said, let's just stop and continue because like, yeah, yeah, yeah, all that. Yes. But so my journey, let's see. When Jen was talking, I was thinking, well, I'm kind of meandering too, but thinking that it's not so much meandering as more like, I just didn't have a particular goal or purpose along the way. And then the sort of like, okay, now I'm going to do this. Okay. Oh, I think I'll do this. And it was kind of looking back. It actually isn't so meandering. It's more like, I did something and then the next thing built on the thing that I had done, which is kind of a nice feeling to have after a while there. So, and in terms of the other thing I was thinking about what Jen was saying about applying to a lot of jobs and, you know, feeling like you have to do all these, there's all of them and there's a lot of work and positions. And I realized afterwards also, when I was an undergrad, and I decided to apply to go to grad school in linguistics, I thought, well, okay, I'll start doing this. And after three applications, I'm like, oh, this is a lot of work. I'm tired of this sometimes. And I didn't apply for anymore. And afterwards I'm thinking, what if I hadn't actually gotten into one of those programs today? That was very kind of lackadaisical. But it all worked out. So what I ended up doing was going to UC San Diego, which turned out to be absolutely the perfect place, because at the time the cognitive science department had just kind of started its graduate level thing, but it was also a program, right, the interdisciplinary program. So everybody had their own, or most people had a different home department. There were people that were in the cogside department. But, you know, I was in linguistics with people in psychology, philosophy, computer science, neuroscience, whatever. And I'm going to be completely distracted by the cute dog. Oh, sorry. No, no, no, no. If I put it down at Wines, it's my mom's thing and it's terrible. I'll just won't look. And it was a great place because I realized that is really a kind of linguistic mind wants to go to cogside to me feels very applied and practical. And, you know, this feeling of, hey, there has to be more than the one field. There's no like the one field to rule them all, right? And that's where cogside comes in. It's like, if you want to actually understand how people work and how the mind works, well, you better have some kind of sense of all those different fields. And just because I'm focusing on linguistics, I still need to kind of have a sense of what's going on at the other parts, right? So that turned out to be really perfect. And my dissertation was one of doing a little tiny computational model of something in Finnish. And also in parallel, asking a bunch of Finnish speakers, native speakers, how they would handle certain sort of nonsense forms. And I realized then afterwards much later that this is kind of my first user study that I just set up. And so here was my initial training on something that ended up doing a lot of and still do and still find the most interesting aspect of my jobs, right? Understanding why users do what they do and try to get the information from them in various ways, right? So I was doing that. And during grad school, I also did some part-time work off campus. And the first job I did was a phonetic marking kind of consulting gig for a place doing speech recognition. They needed to set up a speech recognition database of stuff. So they needed people who had the right kind of training and mindset to sit and do very, very careful phonetic market of some speech files. And that led into the next job, which is a little sort of similar thing, that then became as I was continuing and getting closer and closer to finishing school. And at this point still hadn't really figured out what I'm going to do with myself because it was always like, well, I'm continuing school as a foreign student. I don't have to decide what to do next. I'm just going to continue being a student. And at that point, they offered me a full-time job. And I finished school. That sounds good. I like what I'm doing. So I'm going to go that way. So it was never really a clear decision of I'm going to go into academia or I'm going to go into industry because I just figured there wasn't going to be any jobs doing what I was doing. It was early enough in this space that I thought, you know, I'll teach. I like teaching. Sure. I'll worry about where later. And then this popped up and I was like, okay, great. I don't have to worry about it. So that's how I started with going down the non-academic path, like going to industry. And so that was a small R&D company focusing on sort of language type software and language applications. And I continued working for that company for a few years until it kind of went away. And that's when I actually went to a point of, you know, contact, it's all about contacts of getting other jobs. So when I was at that point, like, oh, I need another job. And then a good friend said, hey, do you know so-and-so there are new ones. I was like, what is new ones? Turns out that was where people doing this kind of stuff went at the time. And so then for the next unusually long period of time, something like 12 years, that was a new ones. And to the point that we would just start talking about job hopping. It's pretty strange these days to be in a company for 12 years in the tech field. But anyway, so I was there continuing doing exactly that. There was basically applied linguistics and cognitive science because we were doing UI design and user experience research and so on. For voice technology, we're doing mainly call-centered types of applications, a lot of the things too. So there was a continuing, trying to decide how do you actually take the tools and everything to make it do what it's supposed to do with users who don't have any training in using this particular thing and make them be successful and testing it and refining it and so on. So very much sort of on the job training applicable, applied work in exactly the stuff that I had done so far. And then I went from there to Amazon for a brief time when sort of pre-early Alexa days. And then a similar thing happened that happened to Jaden, I guess. I got, you know, encouraged to come to a small startup at that point, which I did. And it was also continued to be a similar kind of thing. In that case, it was a mobile app that was multimodal and it was more kind of a good cause, you know, helping patients be, you know, successful in the various surgeries and things like that. But it had a lot of natural language and speech aspects to it. And then I was there, again, we can talk about big and small companies because that's the typical small company. It sort of started, it was around for a while and it disappeared. So, you know, I was there from start to finish. And then when that folded and again, friend came and said, Hey, you know, I hear you don't have a job. How about this? And I got another job and doing some more things again. And kind of the next thing happened similar again. And this is sort of what's happened all along with something happens where, you know, there's a change in the company where it's sold or goes away and again, contact. And again, as Chris has heard me talk about this, you know, the people at Nuance are quite still very much in touch, you know, so that's a very nice network to have. And this sort of a, Hey, you know, I hear, do you know somebody who's looking for something in this field that's talked to so-and-so? And so there's nothing better than, nothing more important than, you know, staying in touch with people and making sure you find out, you know, what's going on and be in touch and just contact, right? And let's see. Anything else? So then, yeah, so now I'm at a small company that's a startup. And I'm kind of the one and only UI UX, everything design person in a group of about 12 or so. And it's, it's fine because also here I kind of, it's sometimes it's challenging, but it's also works out because after a while people kind of get to know each other and it's a little bit about that whole, okay, it's a small company, you have to do what needs to get done, right? And everybody jumps in and does whatever, change the direction, let's do this other thing. And oh, who knows how to do this? Oh, okay, let's do this. And I think it's fun. I mean, having been a big company, a small company and stuff in between, in general, I tend to prefer smaller because I like that broader level of involvement. And, and I do, I try to do as much research as possible because I actually really miss research. And so I'm trying to always do as much of it as I can. And that would be, that is one thing. Well, I kind of wish I were in academia more to do more of that. But of course, there's a grass is greener, right? So I think that's pretty much summarizes it. I just wanted to comment on something earlier, you know, we all have a background and we go into industry. But, you know, in hiring people, and I'm on a lot of university committees for the social impact of computing and whatnot, and how do you train students? And the fact is that 85% of the jobs in the next 10 years, we won't have heard of. And that's true, you know, I've been doing computational linguistics long before data science became a thing, right? And, and it's really how do you train yourself, your students to, you know, just think and be able to do a new job, create those jobs, you know, what is that? And I think that's really important when you think about going into, you know, even academia, because you're training future people. And, and, you know, you don't have to know what that job is. And I think one thing I hear from the three of us is that, at any given point, there was a choice to make. And I think we all like, took the choice that we followed our passion. You know, I have an undergrad degree in philosophy, I didn't know it, and that was the chief data scientist at a company, right? So that's, that's important to keep in mind, right? It's just, you know, where you go, your choices. And you get to help create the next generation of jobs. Absolutely. I also wanted to, I had made a little note, Jaden, when you were talking and you described the academic job market as challenging. That's a kind word. And that's using it's doing a lot of heavy lifting right there, the word challenging. I could think of other words that might be more accurate, like dire, for example. But, you know, the reality is there are far too many linguistics students who are getting BAs and Masters and PhDs, then there are jobs. And it's going to be that way. It's not going to get any better. It really isn't. And tenure-track jobs are becoming harder and harder because many universities are cutting costs and they don't want to pay people. And so all of the faculty is ending up being part time, at will, adjunct. It's, it's very, very difficult. So as what you were saying, Michelle, like training this new workforce that's coming out, I mean, there just aren't going to be academic jobs. And all of these jobs that don't exist yet are not going to be in academia. They're going to be in some kind of industry, even if we don't know what they are yet. So it's just a reality that needs to be acknowledged by everybody in the university system. Well, and I think, I think to add to that too, you know, like there, there are plenty of reasons to get a PhD that don't include wanting to be a professor, right? I think we should be open to the fact that this kind of training is, you know, very versatile, very, you know, easy to apply to really anything that like, you know, I think a lot of professors are looking for students who want to be professors ultimately, right? But I think there, you know, we could be broadening our selection criteria and ultimately looking for folks who are just deeply curious and interested in something, right? And at the end of the day, whether they go into academia or not, I mean, again, more likely than not, a lot of them won't because they're just aren't the jobs available. And I think also because interests change and people by the end of their careers don't necessarily want that path anymore. Maybe they went into it not wanting that path to begin with. And I think, I think there are multiple paths to success. And I think there are multiple reasons to get a PhD that just aren't, you know, academia. I do think we should address and I'd like to ask both the other panelists, you know, so if you're not working in your field, do you regret getting a PhD? Do you need a PhD to do it? And I find my experience, you know, even going in industry, I have, you know, patents, I have close to a hundred publications, you know, I was able to continue that in industry and have teams that write papers and do that for research. But at the same time, I don't know about YouTube, but having a PhD, you get the benefit of the doubt. And, and, you know, you have to prove yourself in a corporate environment. And you do that and you have a PhD, it does matter. It's not, it's not, you know, maybe I could stop with the masters and do just the same. It, you know, I have found it makes a difference. And I'm sure the other panelists have as well. I think that's true. You know, some depending on the organization, it will weigh more or less heavy, because some organizations going to have, you know, especially if the organization already has more other PhDs, they will pay attention in a different way. But just for, just for the pure, of course, I mean, you're going to learn more and you're going to do something in depth and just having that experience of having your own thing that you had to do in depth and presented and do all the work on it. That is invaluable training, right, for anything that you do. And I mean, I'm very happy that I got a PhD. I just enjoyed it. Nothing else. Even if I never even touched ever touched anything linguistic alongside again, I still would have said that it was a great time. But I think it really is that I think it's actually extremely important to teach people or sort of encourage people to learn the underlying stuff or whatever it is, not to say, oh, here, go take, you know, a two week course and UX design or something. Well, guess what? You're not going to do a very good job with your UX design. If that's still that's funny, because I get asked all the time, how do I become a data scientist? I'm going to take course and like, maybe that won't do it. Exactly. You mentioned that. I think that's really important. I think it's something that, you know, we, we can all do well and kind of pushing that more in general to a broader audience because, you know, if you're going to do a good job of any of the stuff that we, the three of us are doing now, or more three of us and Chris too, that, you know, all the kind of applied work that we do in industry, having the background that we have makes for what we produce to be better in general. I'm sure we don't always have, but you know what I mean, because we're thinking about it differently, right? And it's not just, Oh, which method am I going to apply here? You know, I have to do this, but there's like, Oh, why didn't that work? Well, ask yourself, boy, is it maybe there's something else going on here? You know, lots of examples of that, right? But not just sort of be so, it avoids, I think, some tunnel vision that otherwise you end up with if you're just focusing on, you know, the little nuggets of methods or whatever that you may learn. Yeah. And I think, I mean, adding to that too, kind of, you know, directly speak to your questions. I think one, there, you know, there, I think a lot of jobs, most jobs, you know, I say this, and I mean it pretty hard, wholeheartedly, there are a lot of, most jobs in that industry, you don't need a PhD for. I mean, hands down, right? So I think I just want to dispel that myth, right? Like most jobs, you don't need a PhD for unless you're doing, you know, like hardcore machine learning research or something, we're like, certainly biopharma, cancer research, etc, right? Like, you're going to need a PhD, but most jobs, especially a lot of the jobs in tech, you don't need a PhD for, does it come in handy? Absolutely, right? And I think that, you know, I could have come into product management without a PhD and, you know, certainly they're, you know, countless product managers about PhDs, but I think having that additional skill set and know how, and I think a lot of it kind of is much more the, you know, soft skills of product management, dealing with people, you know, relationships, etc, right? And certainly there's, you know, there's a degree of, I guess, awe that people have when it comes to it. But it certainly has helped me in a lot of ways that I could not have anticipated. Again, you know, recognizing the fact that I probably could have gotten the job without it. But I would have done the PhD again and again, you know, I think, you know, kind of the point I was making before, there are a lot of reasons to do a PhD and, you know, I went into it the first time wanting to, you know, get an academic job, but ultimately that didn't end up being the right fit for, you know, where I wanted to go in my life. And I think I still got an amazing training, you know, met amazing colleagues and did some of the best work in my life. And, and, you know, I don't regret switching in that industry. But, you know, yeah, just like, like two different turns. So at this point, we have about 10, 15 minutes off, we might go over it a little bit. I wanted to see if Hadass has some questions from the students who are there at the institute. Yeah, it looks like there are questions. Great. Alastair, do you want to just try? You should be able to. Oh, can you hear me? Yeah, we can hear you just fine. Okay, great. Yeah. So thank you so much for this. Yeah, very, very insightful. So I was wondering, so it seems like most of you weren't sure that you were going to transition into industry while you were still grad students. I'm a grad student now and I'm starting to think about it. And so I'm wondering if there was any type of training or anything you wish they would have or could have done to prepare while you were still a grad student before you were on the non-academic job market? Not for me. I mean, you know, getting, I was worried about getting a job and I, you know, in grad school, I was actually teaching writing at a different university, you know, as my, my fallback plan in academia didn't work out or I couldn't find a job. But no, I think you should focus on your PhD and not worry about it because those analytic skills are what's going to get you hired and help you be successful. And, you know, as you've heard from the other speakers, when you do these other roles, product management, UX design, you definitely can learn on the job and, you know, definitely quicker than most, right? So I actually think, you know, I think I've been an executive, you know, I have large teams, I have 50 data scientists working for me now, most with PhDs. I think, you know, you really need to focus on that, on getting your PhD and those skills and that will help you. You know, I have thought, oh, should I, should I have gotten a business degree? You know, maybe, maybe I could be a CEO somewhere someday. I don't know, but I'm happy with what I do. I love my teams and I think you should focus on grad school, honestly. I think I want to add a little color to that. I appreciate that answer. I think one thing that I faced when I was doing my transition is that there's a lot of people that you talk to in the job application process don't have PhDs. And so I think a lot of, they miss a lot of the context of what it means to have a PhD, to do a PhD. And so there's this kind of like translation problem of figuring out what the right lingo is, what they're right, you know, what they're actually meaning with their question. And so I think part of, you know, part of preparing could be like just figuring out what that translation problem is. But I think one of the other things that I faced maybe more directly was the fact that like, it's a little bit chicken in the egg. So particularly when you're looking for jobs that don't necessarily require a PhD, but where a PhD is maybe beneficial, you know, they want to see experience to know that you can do the job. But unless you have industry experience, it's hard for them to decide to want to take the chance on you. So even if you have like a laundry list of publications or something, right, like they're going to be a little less willing to take that chance unless they know that you can hack it and that you know the like translations and that you can like speak sort of like the industry world. And so I think one of the biggest things that I would recommend and I'd recommend to a lot of people I talked to is and I didn't have the opportunity to do this and you know, who knows if things would have gone differently. But as you're, you know, in your third year of grad school and certainly like kind of nearing the end of your PhD, take the opportunity to potentially do some summer internships. And so those are much lower bar in terms of being able to get into but it's an opportunity both to suss out whether that's actually something you're interested in, right? You could do a summer internship in, you know, data science, product manager or whatever to find out whether it's something you're really even interested in pursuing. Maybe you decide you don't want to do that, you want to try something else. But it gives you the added bonus of having that tiny bit of experience on your resume that when you are flying for those full time jobs, they're like, Ah, okay, I see that you can hack that hack it. You have the right skill set. Yeah, let's move you on. And just just add to that having hired an intern at Google and been on other internship committees. For the company, it's it's an easy way to try you out as an employee. And so it's oftentimes very easy to convert that internship into a full time role later on. Adas, do we have more questions in your room? Yes, great. Yeah, let's try. Hi, I don't know if you can see me. But I'm an undergrad here at UMass and linguistics. And I'm sorry if I don't understand the terms quite yet. But could you clarify what academia really means and industry? Like does that just mean specifically a professor and industry just means everything else? More or less, I'm sorry for asking. Silly question. That's a great question. And we just throw those terms around because we it's sort of our own industry speak. So we are our group. When we say academia, it doesn't necessarily mean a professor. It could mean working as a research person at a university, but we're generally meaning within a university setting. And it could be a lot of different jobs. It could even be working as an administrative position in a university or in a library or in some other kind of research position. And when we say industry, we're just meaning anything that's not in a university. And that's very broad. Sometimes there are crossovers. Sometimes there are industry jobs that are affiliated with universities and sometimes there are university jobs that are done for an industry. So there can definitely be crossovers there. When our panelists today have jobs that are clearly not in a university setting, right? They're working for either large tech corporations or smaller tech corporations, and they are removed from the academic setting. I don't know if anybody else wants to clarify even further. That's how we, within our linguistics beyond academia group, is how we tend to talk about it. Alex, you look skeptical. Did I miss something there? We have wonderful people on our panel who focus on tech within industry. So I just want to assure people that there are many ways to work in business and in government and in nonprofit organizations. There are a lot of other opportunities that have different, that utilize different aspects of your skill set as a linguist. So although we have a focus here today on tech broadly speaking, and that's something that should be defined as well because we use that term, throw it around, meaning all kinds of different things. There are many other ways to apply linguistics in several types of organizations and sectors. I will also recommend people go to our YouTube channel, the Linguistics Career Launch YouTube channel. When we did that launch was two years ago in the summer of 2021, and we had panels on all types of places where you could get jobs. So there was one on nonprofits and there was one under several on government as a matter of fact. All different aspects of industry that are out there marketing, things that are associated with different types of business. So it's a huge range. This panel is focusing on tech because that's where the cognitive science kind of comes in to be with the theme of the summer institute. And I will say very quickly that in other areas, the PhD is not nearly as important as it is for what we're talking about today, from my own experience in marketing and business more generally, having a PhD is kind of overkill. It's great if you have it, but you don't need it, not for the kind of work that I do and my colleagues do. I think for the careers that the folks today are talking about, it's probably necessary because it's just a different field of study. I'm curious if you have any advice for someone, you know, on the fence between academia and industry, you know, what sort of things do you recommend doing to explore and make up? Yeah, I'll jump in. I mean, I'll harken back to what I said before. So internships are a great opportunity for exploring and figuring out what you like or you don't like. But even without that level of commitment, you know, there's a ton of information on the web, a lot of YouTube channels, this one, you know, you can check out that will kind of break down what the different jobs are for you and what you actually do. And there are a lot of kind of day in the life jobs. Absolutely, you know, check that kind of stuff out. Do as much absorbing and learning as you can on your own. I think that's going to be probably your like lowest bar. But then outside of that, like those informational interviews that we mentioned as well, right? So just reaching out to people within your network that do jobs that are interesting to you or sound interesting or, you know, have job titles that you've, you know, like, I don't know what that means. Like, can we talk about it? And again, you know, just a reminder that informational interview is just a conversation. And so I think tapping your network, talking to folks and really just kind of exploring the kinds of, there's so much good content out there. I think you could, you could really go down any rabbit hole to figure out what kinds of things people do in tech, what the job titles are, and then ultimately like dig into whether that's something that even seems really interesting to you. Yeah, ask what people like and don't like about the positions and what they miss and don't miss and things like that. Because it may be something that triggers something in you that, you know, like, oh, yeah, I want more of that, I want less of that, whatever it is. So definitely I would try and talk to as many people as possible in the kinds of different positions that you're sort of balancing between. One thing to add too is that you might not have a choice. So it might not be, it might be the case that you're asking should I pursue an academic job or go into industry? Well, if there are no academic jobs or the only academic jobs you can find are extremely precarious and, you know, don't promise employment from one semester to the next, don't have health insurance, don't have an office, you know, don't pay, you know, very much at all, then the choice becomes quite obvious. And I will also say some people go back and forth, you know, just because you decide when you graduate that you want to, yeah, that you went like Alex did, that you want to get a job in industry, you can do that. And then at some point, if you say, you know what, I'd actually like to maybe go back and finish my PhD or do some research. Maybe your employer will pay for your PhD. That can happen. It really does. And you can be in academia for a while. There are people who are professors and then they do that for 10 years and then they go, you know what, I think it's time for a change. So don't think that your decision is forever. You can change your mind. You can do different things. You're allowed to do that. You can find ways to do both in a way too, more or less informally, right? Even if I'm, you know, okay, so I'm in industry, but, you know, I will work with people that's like, oh, hey, let's write a paper on such and such or, you know, let's do something in this field or whatever. So nothing, unless you're in a position where you really, really can't do anything else at all, because of something about the job that makes up the case, most jobs, that's not going to be completely the case. There'll be some way that you can do other things too. Yep, absolutely. Alex, do we have some questions in the chat chat, the Zoom chat? Yeah, and we'll go back to Jayden for this one. So Jayden, you mentioned looking for roles in both data science and user experience, which can involve pretty different skill sets. Can you talk about tailoring your resume and how you prepped for interviews in different fields? Oh, that's a great question. So I think, I think, so you're absolutely right. So just to speak to the root of the question, right? You're, when you're applying to jobs, right, you're going to have your best chance if you make sure, if you tailored the resume to the jobs that you're looking for, right, to make sure that you can highlight the particular skills that that job is looking for. I think in my particular case, I was looking more for the kind of quantitative side of the user experience jobs, meaning folks that are going to be working with data using large scale data, etc. Right. And so in that sense, a lot of the skills from a data scientist to user experience researcher on the quant side, definitely for translatable, you know, I think it can be a little daunting to feel like you have to tailor your resume for everything, right? So I think I wouldn't go so far as people, people recommend this. And I think they have, you know, varying degrees of success, people recommend tailoring your job to each job that you apply to. You know, I think that's a, in my particular experience, it's going to be very exhausting and probably where you are way more quickly than you're willing to. But so what I would say is just do it by industry. And in this case, you know, I put two together. And for me, fortunately, like the skill sets overlapped quite a bit that I was able to do that. But you know, tailoring doesn't mean you have to have a completely new resume. It just means that some aspect of your resume is to speak to that particular set of jobs, right? So like the, you're always going to have like an education section or like an experience section, maybe some of the bullet points change in your experience section, or maybe you kind of shipped around some of your experience, right? But like, you're ultimately not going to completely change it probably. And especially when you're earlier on in your career, and you don't have as much experience to go about to go, you know, to go around, when you're later in your career, you can kind of pick and choose which experiences you want to highlight. It's maybe a couple of bullet points change, right? Or like, maybe in your skills section, you just add, you know, you list a couple of different skills, right? So it's very minimal tailoring. And a lot of people, you know, it's common to put kind of like a blurb at the top. And so you can certainly tailor that to some extent as well. It's, you know, I don't want to make it seem as if it's like really, you know, a big bear, I think ultimately you want to do kind of as little effort as possible to be able to have the like, you know, you know, I hate to say it, right? But like, you're going to, you know, it's ultimately up to the hands of chance, you know, right? So like, there's so many steps in the process that you have no control over. But like, I think do as little work as possible to get it to a good enough stage. And I think you'll be fine. Other people might disagree. Very good, absolutely. Awesome. Alex, do we have any more questions in the Zoom chat right now? Okay. Hadass, more people in your area there who want to speak up or if they don't have a question, do they have any comments? I have a question or a, well, I have a multi-parant question. So I am curious about for the current job or previous jobs that you've had, would you say that you do linguistics or that you do language broadly and or what skills from your education do you think that you're using now for your previous jobs? I'll do a bit. So I think I'm probably of the panelist the farthest removed from language in my day-to-day job. So I don't do anything linguistics. I don't do anything really psychology of language. I do a lot of psychology stuff. But just because there's a lot of, you know, as you're talking about users and people interacting with products and stuff, there's really a lot of really interesting kind of psychology there. But I do use a lot of the ancillary skills that I learned. So I think a lot of the things that have come in handy time and time again, right? So one way that I talk about this is everyone, you know, everyone has to some extent, you know, varying degrees of, you know, critical thinking skills, analytical skills, etc., right? And certainly, you know, people out of bachelor's degrees get the kinds of jobs, certainly that I do. And then I think some types of jobs that other folks on the panel do. So it's not a prerequisite to have the PhD, right? But I think ultimately with the PhD, we get just a much deeper training in that set of skills, right? So you come out of it much more deeply analytical, much more deeply critical, right? You probably hopefully have like a knack for kind of like data or at least thinking through problems and being able to break problems down, right? And, and certainly, like if you collaborate a lot, there's a lot of like, you know, management of relationships and management of work. If you have RAs and project management in general, being able to see a project from beginning to end, communication, right? Like we're presenting all the time, we're writing all the time. Those are very, very, you know, important skills in industry. And especially as you go up the ladder and move into executive type level positions, you know, all day long, it's about, you know, saying the right things at the right time, crafting the email appropriately, so that you're not, you know, exposing something you shouldn't or, you know, rubbing people the wrong way. And certainly presentations about, you know, you know, trying to convince people to do X instead of Y and the reasons and, you know, create the compelling case for that. And so I think I time and time again use all these ancillary skills that, you know, we've just honed very well over the course of our PhD. And then there, you know, again, they're, they're, they're components of the work that I used to do, right? So like as we were, again, I just kind of read on myself, right? But like as you're dissecting a problem, right? Like as you're dissecting, you know, a certain language or certain structure within a language or something, right? Like the skills that you, you learn around how to do that are going to, you know, be very valuable in how you treat really any challenge or problem in industry. And I think people recognize that and see that when they hire, you know, someone with a PhD, even if they're not necessarily asking them to do something to work on the content of their PhD. But they certainly, they certainly respect the kinds of skills that we have that come along with it. And I think I use those every day, even if I'm not using my content skills. Yeah, I definitely use my skills. I mean, certainly not to point where I would say, Oh, these are things that I learned in second semester syntax or whatever, right? I mean, not like that kind of mapping to what we learned in school. But I mean, I work in language tech, if you want to call it that, right? So absolutely, there's linguistics aspects and, and cogs I aspects that I apply to my work pretty much every day, whether it's, you know, collecting data or analyzing the data or creating, you know, text-to-speech sentences of just phrasing something in the right way for the product, something like that. It's absolutely every day there's some aspect of linguistic knowledge and cogs I that's being applied, I would say. Awesome. We don't have any more questions in the Zoom chat. Hadass, more questions in the room? Alassane, yeah, we have a question. Great. Well, I would actually be very interested to hear a little more about the difference between working at a large company and working at a startup. So some places and working at a startup is kind of terrifying me coming from the academia perspective, because it's like, if you start at full, you lose your job. And I feel like, you know, we have this idea that we're at this one job and that's kind of it. So yeah, I would be really interested to hear from you. Yeah, I mean, I think I mentioned before, there's a little bit of it is, there is a, definitely I would not say that one is more secure than the other, because it just depends. And it's, I personally really, like I mentioned before, I like smaller companies because I feel like have more of an impact than I could get to touch more things and have just more interesting tasks in general. Of course, that depends, you know, what level you're at to in any kind of company. But I, that's what really speaks to me, but small and large companies. That's going to make things go for it. Yeah. So I think, yeah, a lot of it depends on the structure around the job. And that I think varies wildly. So you could have the same job title at a startup or at, you know, a big technology company, or a big company in general, at bigger companies, you know, the job is going to be very well defined, you're going to be in a very clear hierarchy and a very clear structure. It's going to be clear where you fit in in the organization. And you're probably working on a very, you know, delineated set of problems are part of the product. And they're, you know, pretty well established processes in place too. So those are, you know, I think, fortunately for me, because, you know, I didn't know much about project management or product management coming into it, that was actually a really good place for me to start because it was able to provide me that framework for how to do that type of a job at a well established company, right, with really clear, like I said, guidelines in place for how to do it. You know, at a startup, you might have the same job role, but like there's so much more flexibility. So for one, it depends on whether you're the first of that type, or, you know, they're multiple above you. If you're the first, you're ultimately going to have to build out those processes and the structure and everything. If there are, you know, if there's some before you, you know, there's still going to be a lot of flexibility, you might not do all the building out of process, but you're, you know, once you don't build a process once, and it's fine, you know, you're always iterating on those things. And I think those processes change a lot more quickly in startups, especially as the startup grows, and you need to breathe a little bit more, or you're bringing more people in, right? And you have to onboard more people. And then outside of that, too, you know, again, depending on the stage of the startup, but like, you know, you might not have someone in marketing, and you may need to use, you know, where your marketing hat one day, right? You might not have someone who's, you know, there's that random task over there that's that no one's doing. Oh, I'll sign up for it, right? You know, because it just needs to get done. And so I think that the trade off with a startup is that like, especially if you're, you know, a go getter, like someone who's really, you know, a sponge willing to learn, it can be really exciting and exhilarating, because you can just be doing a lot of things and be all over the place and touch a lot of things, and you learn a lot about different, different jobs and what the different jobs do. I think on the converse of that, you might not learn the lines that well, right? So like, I mean, especially as you're kind of all over the place, you don't really know, like, okay, is this really part of my job for you or not? Like, especially if you move to a big company, you might not know exactly where the boundaries are. So like, so it's a trade off, I think at the end of the day. And it's really where you like to get your hands dirty. I think I, again, benefited from having that structure as a jumping off point. So I knew kind of what the purview of the job was, and was able to hit the ground running and kind of build and create a process around it. You know, I think, you know, you could have, I could have figured that out. If I hadn't had that, it just would have taken a lot more learning and false starts. But yeah, to Ant's point, there's like no job security, layoffs left and right, even at big companies. I think the way to think about it is like, you know, yeah, you're not staying at jobs 10, 15 years, some people do, very few people do, right? It's more, you're kind of dispensable at the end of the day, right? You are one cog in the machine. And it's good to keep that into perspective too, because like, go where the work is fun and exciting. And when it's not anymore, find something else. I think you make a good point also about the, you know, as you, if you're, if it's your first job in the space is really nice to have somebody who can show you the ropes, right? So that you don't have to just be the one to come up with all the everything. And maybe you're the kind of person you can, but more likely something is going to just not be right for whatever reason, right? So being somewhere where at least there's somebody who, whether it's a big company or just a place where there's somebody else that's been there before you, who can hold you by the hand. It is extremely valuable to start out. And before we're going to wrap up in a second, we're over time. And thank you guys for staying so long. I think Michelle had dog issues. So she had to go and do something. But I will also say for you students who are thinking about jobs, think about the kind of environment where you might thrive to. And sometimes you don't know and you have to have jobs to figure it out. But assuming that you've had some jobs in your life, like, did you like when you had a lot of structure or do you function better without a lot of structure? I will say for me personally, the first corporate job that I had coming out of academia was at a very small company. And I loved it. And then I did some work for a big company and I hated it because that's just my personality type. I don't like all of that structure. I don't function well in it. I'm much better with what Jaden was talking about, where you're doing a thousand things and you're wearing different hats. But that's you. And you just have to figure out what's going to work best for you. It might be trial and error, but you will find an environment in which you thrive and you do the things that you want to do. And yeah, we're all living in late-stage capitalism. We can't help that. That's just the way it is. But there are places where you can get jobs as linguists use your expertise. It will be appreciated. Sometimes when you pull out that linguistic knowledge, people just look at you like you're a wizard. It's great. It's very, very fulfilling. So I just want to wrap things up now. Thank you so much to Ann, to Jaden, to Michelle, wherever she is with her beautiful little dog. This was a great conversation. For Ann and Jaden, is it okay if we put your LinkedIn profiles along with the show in case people wanted to try to connect with you? And as I was saying before, informational interviews, pinging people to get some information, that's what we're all here for. So thanks to the people on Zoom. Thanks for the people in the room also. Yeah. Thank you so much. I mean, this was really fun. Thank you. I had a great conversation and look forward to talking to anyone who would like some advice. Yeah, awesome. That's great. Thank you. Okay. Thanks, everyone. We're going to stop recording now.