 Welcome everybody to Careers for Linguists. This section of our presentation is all about job hunting beyond academia. My name is Alex Johnston and I'm a PhD linguist with a specialization in socio-linguistics. My research interests are institutional gatekeeping encounters, language and power, critical discourse analysis. But I have spent most of my career outside of academia applying that skillset and that research knowledge base in nonprofit organizations as a corporate talent development specialist and as an independent contractor with my own business and working under another organizational brand to deliver content to global corporations that specialize in ag and biosciences, if you can believe that. So linguists truly are everywhere. If you can find somebody giving presentation and management skills training to scientists in agricultural bioengineering and financial services. I would like to throw to my co-presenter, Chris Stewart, so that he can give an introduction before we get started. Thanks, Alex. Yeah, my name is Chris Stewart. I work as a computational linguist at Google and prior to that did a PhD in French department for linguistics, had a tenure track faculty job in a modern languages department and then worked in Texas speech synthesis and had two gigs as a senior data scientist and here I am now at Google. And I've been doing this work with the LCL for, I'm sorry, the linguistics beyond academia for I guess a few years, three or four years now. Sounds like New Orleans is when this group sort of started to get together and that was, I don't know how many years ago though. I mean, this is the current cohort. But in any case, yeah, and work a lot, trying to help people have kind of a nascent consulting company called PhD to Tech where I try and work with people who are doing PhDs and want to switch to Tech. And yeah, I'm delighted to be here and talk more about my experience. Thank you, Chris. So I just want to mention too that my current position is at Georgetown University where I teach the content that I'm about to share with you in a career management class for graduate students in linguistics. And I'd be happy to connect with any of you after this talk. So what are we gonna talk about? We are going to talk about academic CVs versus resumes, we'll hit on the highlights of resume bullet point formatting and content. We'll talk a bit about locating and pursuing job opportunities outside of academia, which will include some suggested job boards, listservs, some key terms, and how to tailor your resume. We'll also hit on growing your professional network, which is one of the most important things you can do in order to research what jobs are out there, what roles are out there and what people actually do day to day in different organizations applying their linguistic skillset. And we can't cover all of these fully. I spent a semester on this with my students, but we have a lot of resources to point you in the right direction. And this little logo of our career launch Rocketman plus the YouTube logo is a signal that we have a YouTube video on our Linguistics Career Launch YouTube channel. Again, Link is going in the chat to that. And so you can look up videos that are focused more in depth, usually about an hour to hour 45 minutes, a piece on each of these topics and about the jobs and organizations even that we'll be getting to later in this talk. So getting started, I want to outline broad brushstrokes the differences between the academic CV genre and resumes. A lot of you get what the academic CV is. And again, noting that little logo, you can find this presentation in longer form in our YouTube channel devoted to resumes one and resumes two. So a CV, everything you've ever done in your academic career, it's your research teaching service. This is your administrative work, which means a lot of volunteer work. This is organizing conferences. If you're a student, it's serving on committees. It's organizing your student linguistic association, all of that service, grants and awards. And then so many other sections that could include invited talks, conference presentations, posters, publications. This is put into reverse chronological order. It basically remains the same. It just grows lengthier with time and experience in academia. So it's not uncommon for someone who has tenure and is a productive tenured professor to have a CV that is 50 pages, 80 pages, 100 pages. These are novellas of professional career accomplishments in academia. And this means that we can't take this and present it to someone who is a hiring manager in an organization outside of academia, in business, in the federal government, in a nonprofit organization. We need to use a totally different genre. And that's what we call in the U.S. resume. So let's talk about resume format and how that's different. And I do want to let you know that this doesn't mean that you scrap your academic CV. That experience is very important. The experience is important, but how you write about it, how you frame it and how you edit your accomplishments is going to be quite different. And we'll see some examples of that. So your resume genre, that is a curated one to two page document of skills and accomplishments. And you're going to tailor that to each and every job application. This may sound like work, but it gets easier over time, especially once you've created a base resume or what I like to call a comprehensive resume. I'll get to that in a minute. You need to revise your resume to tell a different story, possibly with a different frame, depending on the sector and the organization you're applying to, and use different vocabulary for each application, each job application. And when it comes to experience, the good thing here is that this includes your academic experience. It also does not mean only full-time, part-time paid employment. Experience when it comes to working outside of academia could be volunteer work. It could be unpaid work. It could be a course project that you accomplished independently. Or with a team. It could be an independent project that you took on all by yourself because you wanted to teach yourself how to code or how to query or how to create curriculum. It could also include professional development skills training. So if you perhaps used LinkedIn Learning and went through a module on how to, you know, present leadership skills, how to work on querying a database. Any type of learning where you work on specific skills is something that you can reference in some form, depending on the application. The main thing is to remember is that your resume is this document of your skills and accomplishments. Basically, the actions you took and the results you achieved. So you always want to keep that in mind, actions and results. Now I mentioned earlier, you want to tailor your resume. You have to think about your reader, first of all. And let me just expand upon what I mean by reader. You may have multiple readers in a typical multi-stage hiring process for a large organization. And definitely within the federal government and with some of the larger organizations in private industry, your resume may first be viewed or read by an applicant tracking system. So there may be a computer that is scanning your resume and it's actually looking for the degree of match between the words you use to describe your skills and accomplishments and the description in the job announcement. So this is why it's really important not only to tailor your application using words from the job announcement to appeal to the computer, which is probably looking for a percentage match in order to forward your application on but also to the human readers who are coming along down the line. So the people who are coming along the line in some cases could be very general recruiters who are checking your resume for the required or desired skills and requirements for the job. And it also may later be a content area specialist or the hiring manager for that particular position. So you're gonna have a lot of eyes on your resume from different people potentially, but your main guiding document for tailoring that resume is going to be the job announcement and all the networking that you're gonna do, which we'll talk about later. So basically getting back to the CV genre if that's what you're starting out with that level of detail in that multi-page document if it's five pages or 10 pages, that's not gonna be pertinent or relevant to hiring managers in say tech or almost anywhere really. So you have to re-engineer what's on there. Basically in a way you kind of have to rewrite from scratch. We'll talk about some tips for that, but I wanna throw to Chris because Chris is gonna present an example of how he reduced four and a half pages of his academic appointment, his publications, conference presentations and classes to three very carefully crafted bullet points. So let's see how Chris did that. Yeah, so this is, I have a website where you can read my academic CV. It's christenformsteward.com if you are curious, but that has the long, long, long version of this, which is all the articles that I published and all that sort of thing. But this is four years on the tenure track on my current resumes. This is assistant professor at University of Texas at Arlington and then some summary of what I did. I managed projects related to research instruction in course direction, which is sort of pertinent for someone in tech, which is the sector that I work in. And then what I did, I carried out a nice verb, carried out original research in French, English and Spanish, resulting in five publications, five conference presentations and two invited talks, partnered with three faculty in three departments to author a well-received neuroscience funding application and oversaw a team of 10 instructors that improve student outcomes dramatically and raised enrollment by 86%. So I try and give some nice concrete figures there at the end and bold here you have the, this is what you want to do and Alex will be the expert on this, but you want to use verbs like strong verbs. I managed things, I carried things out, I partnered, I oversaw. And then some in the blue you have the, the some numbers, like some sort of facts, right? Some artifacts to back this up, the language, the amount of things and different departments. So this shows an ability to work cross product area is what we would call this in tech. So across different departments. And then, and then some nice numbers. Like I saw 10, like I said, 10 instructors and raised enrollment by 86%. So this is a concrete outcome. Exactly. So I also like that Chris starts this section with managed projects. That's pretty strong. And you know, there are position titles called project manager. And this is a position that linguists can do depending on the responsibilities in the relevant organization. So showing that Chris conceives of the work that he did in his academic career as managing projects is something that someone as a hiring manager outside of academia will recognize and say, oh, I understand he's in this leadership position. He did independent research, instruction, instructional design. And that's a way to bridge the academic and the industry language managed projects. He does start out, as you can see, with action verbs and then presents a quantification. So to back reverse engineer the genre here, what we're asking you to do is to present action result format and content. So start with that action verb. And you can Google these resume action verbs and find them online about, if you're stuck for these action verbs like managed, led, developed, drove, delivered, created, you can find lists of these online to spur your creativity in picking a judicious variety of these verbs. So use Google to your advantage here. We want you to show your skills and results. What we wanna avoid is listing your responsibilities. Too often I see resumes where people have listed the responsibilities for the job like teach Ling 101. That's a job responsibility for teaching assistant perhaps, but it doesn't quantify a result. To quickly convert that kind of experience, you could say something like created 25 unique multimedia presentations per week explaining complex research concepts to general audiences. Yeah, that sounds like teaching undergraduates about linguistics and that's a transferable skill, breaking down a complex concept and presenting it to a general audience so they can understand what you're saying without jargon. That's an action and a result. And that's also a skill. So that's an example of how you can convert teaching experience to a resume bullet. Again, we'd want to see as much quantification and description, judicious description. So Chris had a lot of numbers in there. You could talk about the numbers of students that you have taught or mentored. If you don't have numbers, you could say as Chris did, research in French, English and Spanish, original research conducted in French, English and Spanish, right? That's descriptive. It also shows that you have multilingual skills. So don't take any of your skills for granted, really dig deep and think about what you can do as an academic and be explicit about it. Research skills, break those down to the qualitative and quantitative research methods that you know and have applied to obtain certain results. Talk about your effective oral and written communication by showing how many presentations, multimedia presentations that you created and presented. Talk about your critical thinking, your systems thinking. Not everyone has these skills. We get used to them in academia because we're surrounded by peers and colleagues who have developed similar skills. But outside academia, these are what 90% of employers are looking for. They're looking for effective oral and written communication, critical thinking and all of the other skills that we take for granted as academics. And then finally, you want to use that job announcement as a resource. That's your source document for the type of keywords that you want to include in your resume and try to think about how you can frame your bullet points by incorporating some of that language. So as far as how you locate those key terms from the job announcement, this is where your discourse analysis skills come in handy or your linguistic analysis skills. Notice what terms are repeated, what terms seem to be important and highlighted and the focal point of a sentence. Look at terms that you may have an idea of what they mean or maybe you have no idea what they mean and highlight those as ones to figure out. How do you figure them out? You need to talk to people. So informational interviewing is one of the best tools that you can use to figure out what are key terms, what are the vocabulary items, the lexical items that you need to know in order to sound like someone in the organization or the sector that you are writing in a resume for. So for those who aren't familiar, informational interviewing is a recognized technique of job research in the US. And it means that you're not talking to people and asking them for a job. It means you are reaching out and connecting with people who are doing a job you might have an interest in and asking what they do day to day, asking how they found that job, asking what is the culture like at their organization, asking about their responsibilities, things that you can't find on the website, things that you can only find out by talking to somebody who is doing the job. So if you need guidance about how to do informational interviewing, we'll add to the Google drive of resources and informational interviewing etiquette guide which gives you a roadmap to questions you can ask of people and how to reach out to people. And Chris is gonna talk about that later too about productive networking pathways. Another way to learn about what terms are key is to research using LinkedIn. So LinkedIn is not only about presenting yourself to others, it's about making use of this database, this huge database of resumes that are freely available online and being able to look at people's profiles and see their career pathways, how they went from degree to their first, second, third position, at least what they choose to post. That's a very important source of information. It's a way to learn about specific people who you may wanna talk to and it's a way to get a general idea about some certain career pathways as well as to read about what people do and how they talk about it. So again, you'll find key terms appearing over and over if you're exploring instructional design or if you're exploring user experience research and design. You'll start to see repeated terms and that's where your skills as a linguist, your skills in pattern analysis will really help you. So your linguistics training is going to be useful in your job search. Another thing about terms, just as we have terms in linguistics that are used differently within the field as opposed to by laypeople, like say, politeness. There are terms that mean different things in different industries and different sectors. So you gotta try to disambiguate those terms. So for example, a development manager in nonprofit organizations is somebody who focuses on fundraising. Development meaning raising money for the organization. Somebody who is reaching out to potential donors, cultivating donors and getting that money that nonprofits need to run. Development in tech though means something totally different. This is Chris's area. Yeah, sure. So the development in tech is not about fundraising. It's about building a product of some sort by incremental improvements, so rolling out different version, releasing intermediate versions, user testing, bug fixing, all those delightful things. So it's a very different sort of use of the word development. And as Nancy just hopefully added in the chat, business development, that's a whole different use of development. We call that BD for short. This is something I had to engage in as an independent contractor. That means you're finding collaborators, partners, you're finding clients and you're convincing them to buy your services. So business development is usually what you're spending 90% of time on if you're an independent consultant. So again, we need to disambiguate these terms and some of the best ways to do that is through search and through talking with people who are in those industries and other development as well. Yep. Now, we have to take care of this low-hanging fruit right here. Researching job ads and defining those searches by key terms, you don't wanna look only for linguists. If you look for linguists, be aware of what that word means in different sectors. So for example, in the US government, the definition of a linguist is someone who has fluency in a language other than English. So my father was a Korean linguist in the US military. So he's a linguist, I'm a linguist. We do very different things. So you want to be aware if you're applying for federal jobs, that's primarily what linguists signifies. Somebody who can look at data in another language and extract meaning and insight from it. There's a huge variability in what linguists means in quote unquote technical job ads. So it can mean anything from a social scientist to a software engineer who may have had some linguistic coursework, right? So this is all to say you need to, when you're researching job ads, looking at those listservs that we're gonna tell you about, you need to expand your keyword search well beyond linguist and language and communication. And we'll give you examples of the type of position titles that you'll find coming up. Well, we're everywhere. Actually, we have been employed in all organizations, all sectors of employment. And in many, many organizations where you might be surprised that linguists are employed. So let's go over some places where you'll find linguists and what those areas of employment are and what are some sample job titles. And these job titles have been pulled from my personal experience of my, the graduates from the Masters in Language and Communication at Georgetown. So these are all people doing work that I know personally. For example, in this large catchall category of business or industry, which includes so many things, there are areas including branding and naming. Our own Laurel Sutton has her own naming company. Client services, marketing, analytics, medical marketing research and doctor or healthcare provider patient communication. Some position titles that are associated with some of those areas could be language strategist, strategist and linguist, market researcher, marketing analyst, analyst, language data analyst. Do you see some terms recurring and other terms not recurring? There's also a typical job pathway of research assistant, higher level coordinator, higher level manager, higher level director, more or less that could be how seniority pathways go in some organizations. Very few job titles here include the term linguist or language or even communication. But we do see researcher and analyst, there are a lot of applied research positions out there where you can do the work, the type of work that you love in academia, data collection, research design, analysis, you can do all of that outside academia as well. More areas in this large catchall category of business and industry that are by no means all of the areas that I could touch on. But again, you can see the different areas where linguists have been and are employed and different titles. And here we don't have linguists at all, human resources specialist, DEI specialist, curriculum or instructional designer, corporate talent developer, that's a thing that I did. And here's some examples of actual organizations who employ linguists. We see some healthcare, business to business, insights and analytics companies like Verilog, InVive and Ogilvy that focus on healthcare provider, patient communication, for example, as part of their work. We see large consultancy organizations like Boozelle and Hamilton, government contractors like MITRE and the big ones too, like Chris's organization. We got Google, we got Apple and then we have the feeder organizations that often prepare people for work at Google and Apple. Moving on to nonprofit organizations, there's a lot you can do in nonprofits. You can focus on research and research translation and you might be called a strategic communication coordinator for doing that type of work. Again, taking research or any type of complex concept and developing messaging geared towards specific audiences and stakeholders, a strategic communication coordinator. We have linguists who do scientific and technical writing and that's a self-explanatory position title. We have linguists who are involved in broadcast and podcast media, so media production and digital storytelling. So they're gonna have titles like producer or assistant producer. And actually linguists are very well set up to work in what we might think of as traditionally journalist roles because linguists as specialists in pattern analysis can spot trends, can spot newsworthy items and trends and pitch stories. So these are things that we know how to do as linguists and we're competitive for those types of positions if you have an interest in broadcast and podcast. And we can also work on advocacy that's very important to a lot of students in my program being able to advocate for particular social or political or environmental causes. And for that, you could find yourself in a nonprofit organization as a development manager, crafting messages, as a communications manager or as somebody working the social media channels. And these are examples too of some nonprofit organizations that employed linguists and you'll be able to see our slides later. So you can read about those. A lot of linguists are working in areas of the federal government and at other levels of government as well, state and local. We have a linguist who works at the Census Bureau Center for Behavioral Science Methods just joined us for the career mixer to talk about the work that PhD scientists do within that section of the Census Bureau. And of course, there are the traditional three-letter organizations that employed linguists for language data analysis and other multilingual work. Now we get into tech and I put this in quotes because again, this is a very broad catchall category that we have to be careful about defining but a couple very broad areas within tech that a lot of linguists go into are user experience research and design with these shortened acronyms. And you can see that there are many position titles associated with user experience, research, UX writer, designer, design researcher. They're all combinations of research and design. And similarly with linguists who write the conversational flows for website chatbots and for voice assistants like Siri or Alexa, we have these type of job titles associated with those areas. And again, none of these necessarily include the word linguist or language. And yet the application of the skills is entirely from your linguistics training if you've taken any discourse analysis, any conversation analysis. And of course, you don't even have to have specialized in those areas. General linguistics training is sufficient for getting in on the ground floor of conversation design. Now, where to find all these jobs? Chris and I compiled a list of some common listservs. They're here for you. These are ones you wanna get familiar with so you can put in some keywords and see what results are returned. Try to experiment getting to know these different listservs. Moving on, I want Chris to be able to present an example of a job announcement. Okay, so this is actually a job that I had. So I can talk in good detail about it. This is the first job I ever had outside of academia. In fact, the title was actually voice engineer. So I don't know if that changed from the time that I copied and pasted this into a text document or not. But in any case, this side really instantiates a lot of the strategies that Alex mentioned earlier about applying some critical discourse analysis and discourse analysis rather and an early linguistic analysis to a job ad. So the first thing you wanna do is you see that it's at nuance. So the first thing you might ask yourself is, what can you find out about nuanced communications? If you Google now nuanced communications or Bing or whatever search engine you like, you will find that nuanced communications no longer exists. It's now called Sarance. So that's the first thing you could find out. But there's also a site called Glassdoor that you could use to find out how people liked working at nuance. Do they have positive experiences or negative experiences, that sort of a thing. After nuance, you see that the job is in Belgium. Are you up for moving to Belgium? That's a good question to ask yourself. And then finally, do you have any linked in connections to nuanced employees? So this is really critical, this sort of networking piece. And we will come back to this, but this is one sort of key. If you were interested in this sort of a job and you found this job, you know, adverts somewhere, you could look through your LinkedIn connections to see if you have a first connection to LinkedIn or LinkedIn will also allow you to do skip connections. So you can say, you know, I know this person who knows this person who works at LinkedIn, for instance. This job in particular was all about text-to-speech products. So text-to-speech also called TTS is an area that you can do some research on. There's a book, I have it somewhere around here called text-to-speech synthesis. And so you could find that book and read up about what text-to-speech synthesis is and see if you are interested in working and it's kind of an interesting, it's basically talking computers. So any sort of machine that talks has a text-to-speech synthesis component. So reading a little bit further down, you see that, sorry, it's sticking in the headline here. It's a voice developer. And if you sort of parse the verbs that are used here, there are things like develop, migrate, existing, support, building, testing. There's not a lot about research. So if your objective is to get a job as a research scientist, this is probably not really the job for you. So that's a good sort of way to figure out this part of this job ad. Alex, can you advance the next slide? Thank you. So moving on, so the qualifications here are working in Windows and Unix and Linux environments, strong sense of precision and quality in your daily job, fluent in English, understanding of phonological and phonetic concepts, et cetera. You see that, this seems to be a lot more about analysis. So interpreting spectrograms, writing high quality documentation, problem solving, et cetera. It doesn't really read like a job that requires advanced programming or statistical modeling skills. So if you are a computational linguist, this is probably not really the job that you might not be interested in this job, rather. Or rather, that's a red flag, to sort of say, hold on, maybe this is not quite the, but it's a question that you could ask in an interview, for example. The qualifications here are heavy on validation and processing. So this is the analytic skills, troubleshooting, those sorts of things. So it appears that the successful candidate would be processing large amounts of speech data. And if you're interested in applying for this job, ask yourself, is that something I've done? Is it something I would be interested in? Or I do something completely different and really don't care that much about speech. Then even if you're desperate for a job, this is probably not the job that you're gonna get. So don't waste your time going after it. It requires the job, the qualification, the educational qualification, is a master's in phonetics, computational linguistics, or another related field. So again, this is not a research science job. So if you want a research scientist position, this is probably not the job for you. I think there's a third page, yeah. So desired skills, so this is, we get lots and lots of questions about this. Let's do, I need to know how to write code. So this job ad for the desired skills says basic experiences, basic experiences with scripting languages like Perl or Python. So it sounds like you don't need a lot of experience beyond basic sort of scripting languages. If you're looking to get experience with machine learning, this doesn't really sound like the job for you. So that's a thing to keep in mind. The concept of text-to-speech voice development, if it's probably not something you've ever heard of, it's a relatively niche field if you're coming from an academic linguistics background. But it looks like this is a relatively low-level job, so maybe a good place to get your foot in the door. And overall, if I had to sort of read through it, it seems like this is a good first non-academic job for a funatician, which is in fact, exactly what I was and exactly what happened, so yeah. So this brings up the point of, so matching your resume to this job ad, you want to think about what can you say that you know? So I get a lot of questions about this because a lot of students will do some sort of statistics at some point in their graduate work and you want to know about it. You get a lot of questions about if you're doing statistics, you're probably writing code and you want to know how strong can I make my claims about statistics and programming be. So on the one hand, I work with someone who is a quantitative psychologist who can tell you the history of the p-value inside mountain, 17 different ways to conceive of it. That's not really us. Linguists, I'm not saying obviously, we should understand if you're doing quantitative linguistics, what a p-value is, or statistical analysis of some sort. But probably we don't have that level of expertise, right? This guy did an entire dissertation and had an entire research career on this subject. I can't make claims as strong as this person's claims can be. Ditto for programming, it's sort of the same thing. There are people who go to school and that's all that they do. So while you may have written some code, it's difficult, I would not want to make the claim that I am a super, super strong programmer, even though I've written tens of thousands, hundreds of thousands of lines of code, because there are people that have written millions and millions and millions of lines of code, right? So oftentimes what I hear is students make unreasonable claims or something like, look, I know that I'm probably gonna try and get a job at tech, but I started a machine learning class and all they talked about was regression. And I've made a regression model. So I'm an expert in machine learning. Well, that's a really dangerous claim to make because you're gonna go into an interview and you're gonna say extremely strong in machine learning and you're gonna come across someone who did a PhD in machine learning and guess what? You're gonna quickly figure out that you might know regression, but you don't know regression, like you don't know machine learning like they know machine learning. So that's one extreme. So the other extreme is to fall into self-deprecation. So you know that there's a coding interview coming up and you read this book, cracking the coding interview and you think, oh, well, it turns out I've written some code, but I don't know anything. There's no way I'm gonna pass this. Well, cracking the coding interview is for software engineers and you're probably not going to get a job as a software engineer, at least not with some significant retooling. So you have to sort of find the just the media, the sort of middle ground here. Like what can you say you know without saying you know too much? So that brings up the question, what should you say you know? So you need to find the overlap between your skill set and the skill set, the skills listed in the job ad. And if there are gaps between the two, you know, don't panic, but be sure you understand what those gaps are. And I'll talk about one in just a second and indicate that you're excited about learning new skills on this job. So coming back to the previous side, you want to be able to talk about what you did and try and talk about how to take it one step further. So I talk a lot about, you know, the going from inferential statistics, which is probably what you've done, sort of, you know, the typical graduate work in linguistics to statistical learning. So what happens when you have tons of data, so much so that you can't really put all of it in a model, while you have to do some different stuff. And there's, but there's, so that's the intermediate between inferential statistics and machine learning. So the one thing that I think I hear people say a lot is well, you know, this job ad mentions SQL. What is SQL? SQL, I have no idea what that means. Well, you know, don't, first of all, don't panic. SQL is sense for structured query language. We are linguists. This is in our wheelhouse, right? In tech, almost all data lives in databases. So if you want to access that database to even do the simplest thing, you're going to need to write a query. If the job requirements mentioned SQL, you may be expected to be the person writing this query. If not, there may be a UI that runs the SQL that someone else has written. But regardless, 90% of what you actually need to know, you're going to learn on the job. So you just need to know the basics and suggest that you're excited to go deeper. But just to give you some proof here about the SQL thing that I'm talking about, you go to the next slide, the next slide out. So look, if you want to say, you say, oh my gosh, I have to deal with the database, this is miserable. Well, if you want to just know what's in the database, you can just do describe data table. This is kind of pseudo SQL that I'm writing here. You say, look, that's great. But really all I want is the data from my experiment. Well, you just do select columns from the table where, for instance, experimental ID is blah. And you say, well, that's great, but I need some information from another table too. Well, you just select the columns from table one and table two from table one, join table two on. And this is like the rows in this column are the same in these two tables. And then this, again, this where cause, where experimental, blah, blah, blah. So if that doesn't make much sense to you, it doesn't really matter. The goal is just to show, this is really the simple parts of this are not that complex. So don't get overwhelmed if you think, oh, the job ads are SQL and I don't really know much SQL. You can learn what you need to know relatively quickly, just to get past this initial coding interview. Thanks, Chris. Interlude. I know many people will have to leave on the hour. Chris and I will plan to present the last five slides of our presentation for the purposes of the recording so that that can live forever. So anybody who needs to leave to catch word of the year, please feel free and leave any questions for us in the chat or connect with us later. Now, if anybody just listening to Chris's previous slides did feel a little overwhelmed, I'm here to speak to the sociolinguist and the general linguist in the house and look at a different type of job ad altogether. This is not to discount that you can learn these types of things on the job. Most of what you've learned will be on the job. So even if something seems very unfamiliar, most likely you can learn it, you've learned harder things. But let's look at a sample job ad for, that's I think pretty relevant to a socio or applied linguist. And this is as a, let's ignore the rank of senior manager, but this is a position in user research at Comcast. Yes, Comcast employs linguists to do rather ethnographic work. The user experience, the user research team employs a variety of techniques, including concept and usability testing in our in-house labs, field research in customers homes, remote testing, surveys, industry research reviews and more to gather data and develop insights critical to improving our customer's experience with our products. We work closely with multiple teams to identify opportunities to research. So using our analytical skills, field research in customers homes. Wow, this sounds like a socio-linguistic or ethnographic field work type of position where you at least get to do this some of the time. Sounds like a socio-linguistic interview, actually a semi-structured interview which we're all pretty familiar with, which many of us are familiar with doing. Keep in mind though that this will be a faster process and more goal oriented rather than open ended qualitative research and it will be very focused on product. So it'll be very, very goal directed in industry. Saying gathering data and developing insights, that is analysis and that sounds like research too. This is something we know how to do but it gives us a clue too that we could talk about developing insights rather than doing analysis per se. Not that it's wrong to use that term analysis but looks like develop insights is a key word here. Also the work closely with all of these other teams that shows us that teamwork is essential and indeed all across industry and in government and nonprofits, teamwork is essential. Being able to speak to cross-functional teams and be understood by people with very different backgrounds and job descriptions within that organization is essential. So learn their language and perspectives and learn how to speak to others clearly. Responsibility, need primary research studies and customers homes including discussion guide development, participant recruitment, preparation of testing materials and prototypes from other teams. So wow, this sounds like a socio-linguistic field methods glass actually developing the research design for studies, going out and collecting data, developing rapport with interviewees. It really does sound like something that we're familiar with doing. And again, the repetition of teams that shows that this is really important. And this is something that we can highlight in our resume by showing how we work with others in doing our research and our academic work as well. Analyze findings and synthesize with past relevant research. Yeah, we do this. This is like a lit review. Seeing what's out there, seeing what the gaps are, seeing what the trends are, seeing where our work needs to fill a gap but we have to do this on a much faster timeline and keep to the language of industry. So these terms all give you clues about how to rewrite or write your bullet points. So there's a bit of a shift in perspective from academic research to applied research. You can definitely do research in government, in nonprofits, in tech organizations. Research is happening all the time but there's a shift in perspective there. The research questions are driven by the business and or the client needs. Much of most of the same tooling applies including finding evidence like statistics and producing artifacts like reports sometimes, or white papers, but the research is hidden from us. Most of this research is not available externally because it's intellectual property. So just as we have some academic research that doesn't make it out to the general public or to other academics, do the paywalls, et cetera, we don't have access to all of the research that's happening outside of academia, but it's there, it's massive and they need people like us who can deal with massive research projects and data. I'd like to suggest you don't be your own gatekeeper with respect to job ads. Again, remember what Chris said, know what you know and don't know, but remember, most of your learning will be on the job, you'll be onboarded, trained, you'll learn what you need to know when the need arises and learning never stops in jobs outside of academia. And that's a good thing for those of us who are lifelong learners. This is part of the reason that we love being in academia, we love learning new things and constantly figuring out problems and applying our skills to problems. So, you know, Russ assured that the things you like about academia, if you boil them down, you can find those things outside of academia too. Always remember that academia doesn't have a lock on research, on teaching, on mentoring or on administration, that all takes place in other industries and organizations. Chris has put a surprising number in here. Yeah, I just put this in here just to sort of suggest to you that you will have to apply for lots of jobs to get a job. So, I would estimate that I've applied to maybe 500 jobs and maybe gotten about 20 offers, but it is important to keep that in mind. There will be a lot of failure. I mean, you will not get a hot of the jobs you apply to. But, you know, a note of reassurance is that as you get more and more senior in your field, the jobs start to come to you and the number of apps, the number of job applications per, you know, to offer decreases by a lot. Yeah, significantly, significantly. And the key thing too there is the networking that happens. Once you get to know your industry, your organization, that's where you find out about these other opportunities. And this is why expanding and cultivating your network from day one of your linguistics program and day one of your job, whatever it is, is essential. So, if we're talking about careers outside of academia, you definitely need your LinkedIn profile to be up there and polished. You can easily search for LinkedIn, you know, 31 ways to optimize your LinkedIn profile. Go ahead and do that and work on it. Take schedule in that time to polish up your profile. And then use LinkedIn to research career pathways and to connect with people. And when it comes to connecting with people virtually through this platform or other platforms, you know, slightly different for different platforms, but on LinkedIn, it's definitely not stalking. Don't consider it. A lot of my students think it's stalking to look somebody up on LinkedIn, learn about them and connect with them. No, it isn't. That's why those profiles are out there. But there's kind of some etiquette to do that. And I recommend that you first get familiar with the process of connecting by starting with alumni from your institution and the rest of your inner circle. It can be your faculty. It's not strange to connect with your faculty. This is not Facebook or Instagram, which may have different connotations about connecting with people in positions that are higher in the hierarchy than you. So by all means, connect with your faculty, people that you know, and then gradually start to interact with and cultivate other connections, the people who are one to two degrees removed from you. That's where you'll really engage in learning about opportunities, about career paths. That's where a lot of your new information will come from. That one to two degree of separation, those weak ties that some people talk about. Mentioned before, make use of handshake, alumni association, career training, and informational interviewing. Again, we'll have that etiquette guide put out in our materials. Chris is going to mention a successful networking pathway, a model that you can use to developing a relationship with someone. Chris, would you like to explain that? Yeah, sure. So this is an interaction that I had with someone at the Linguistics Career Launch this past summer. And in that sense, it felt organic. I mean, I put that in quotes here because it wasn't really organic. I mean, we engineered the event and this individual showed up and we talked and it felt organic. The student actually stayed in touch. So I do lots, loads and loads of informational interviews and sort of mentoring type work. And at the end, most students will say, oh, yeah, well, I'll be in touch. And then I never hear from them again. That's 98%. But this person actually stayed in touch. They were clearly ready for a sort of a high-powered tech job, a tech job that was relatively difficult and asked to be recommended for a job at Google and they now work at Google. So yeah, I think that what I would say here is that sort of felt organic. It was never kind of weird. Like no one said, give me a job at Google. And you sort of have to be ready for the job. So the job is clearly a technical job and you have no technical skills. There's no real point in asking for a recommendation for that job. But on the other hand, that's not to say that you shouldn't ask for recommendations. Just make sure that your sort of profile is well aligned with the position so that the recommendations just put you over the top. Thanks. Some takeaways from our presentation today and things that you can take forward and get to work on, DB. Try creating your resume from scratch. This doesn't mean throw out all your experience. It just means to take a look at some clean, simple resume formats and start writing out those effective bullet points, action, result. And try to think of all the experiences that you've had, volunteer work, unpaid work, independent projects, to make into a comprehensive resume that captures everything you've done. Then you'll have that to work from and to curate for a tailored resume for a specific job announcement. So I'm just recommending that as a strategy to put in the work now to create a comprehensive resume and put everything in it. You won't use it all every time. Again, you wanna make sure your resume is one to two pages. One is perfect for most things. And try out some of the different job boards that we recommended. Use keyword searches that are broader than linguist language communication. Try combinations of keywords and then see what job ads you're getting. Research and analyze them. Find what terms appear over and over and then try to define those terms. If you have any questions about which nuance of development is being used in which job ad, talk to people. Talk to people who know. Reach out and get on LinkedIn and look at those career pathways. And again, how people write about what they do. Get a sense of the language that they're using. And try connecting. You have already met us in this presentation. You're welcome to reach out to us on LinkedIn and bridge by saying, saw your presentation at LSA. Just wanted to connect. And I will immediately connect. I know how we've interacted in the past through here through this presentation. I will happily connect with you. And so will my colleagues. So here are more resources. Again, these have come through in the chat. And remember tomorrow, we are having one-on-one mentoring for those who pre-registered and drop in office hours for anybody. Join us on our gather platform. We'll be able to interact some more. Make sure to connect with us. And now I'm gonna stop sharing and thank you so much for your attention today. It was a pleasure to interact with you and keep in touch with us. Thank you.