In Fall 2020, the Department of Linguistics hosted an online professional development workshop that featured speakers who are alumni of the department: Nyssa Bulkes, Stephanie Landblom, Brad Miller, Fred Davidson, Erin Rusaw, Yuancheng Tu, and Chris Stewart (alumnus of the French department). Our alumni shared valuable tips and suggestions to help current students prepare for a career in industry. Read on for a summary of the Q&A portion of the workshop, or click on the headings to watch the video clips from the workshop to hear directly from our wonderful panelists!
Q: Were there any skills that you felt you were missing when you transitioned to industry jobs? (i.e., any classes that you wish you would have taken while still a student to be better prepared, etc.)?
Panelists: First, ask yourself, “what do I want to do?”, if the answer is you want to get into big data, analytics, then you should try make sure that the classes that you choose are going to be impactful in your time. If you don't know Python or if you don't know R, try to take Introduction to Computer Science or Introduction to Computational Linguistics, something that's going to get your feet wet and get you in the door. Make use of resources such as DataCamp, Lynda, or other online resources to pick up new skills like Python and SQL. Don’t just rack up certifications so that you could post on LinkedIn. Find a project on DataCamp and work on it. There are all kinds of open-source data sets, so pick something and then use the skills you learned to produce something, for example you can produce a cool and shiny app. Do something, have something tangible to then show the recruiter or if it's a completed project, you can even put it on your resume.
You just need one or two programming languages that you can use to show your capability so pick one or two and focus on that. Besides, companies also look a lot of projects you do when you are a PhD student to know what kinds of projects you have been doing, what kind of skills you have learned over there. So people look a lot of things as well when they look at your resume. You should also go to recruiting events. Get to know your recruiter, proactively sell yourself to them, show your face to them. Also always be prepared for interviews.
As a PhD, you already have marketable project management skills (project control, project planning). Take the time to step back and look at your dissertation from an oblique angle and figure out, well, what are the project managements skills that I've put into that? If you can do so, diplomatically encourage your advisor to discuss project management skills in a reference letter. In addition to having natural project development skills and management skills in working on your dissertation, it's good to seek out opportunities such as sitting on the item and test development team for the English placement tests at Illinois.
Q: What is the expected way for job applicants, internship applicants to interact with recruiters, particularly on LinkedIn? Do we just connect with them or do we follow them but not connect with them? Do we send a message? What is the expected way to navigate that space?
Panelists: You have a wonderful network at the University of Illinois, and it’s not just people from the Department of Linguistics. You have a wonderful, wonderful employee network that you can tap into on LinkedIn. Once you find those companies that you're interested in applying, they all have online application portals and you can apply there. So you should generate pull from within the company through your networking and at the same time show that you've got the initiative by applying online. Also in interacting with recruiters, you should make your request as focused, targeted and realistic as possible.
Q: In addition to Coursera, DataCamp, LinkedIn, are there any other particular conferences or workshops that you would recommend that maybe offered annually for us to go, particularly in fields like data science.
Panelists: There are workshops and camps for R, Python, for example, the company that builds R Studio, runs camps like that. There are all sorts of Python, data science like machine learning, everything from math to more sort of data manipulation and munging and that kind of things. Some of them are very expensive and some of them are much less expensive.
If you're on campus at the University, they have monthly data science user group that meets every month. And there are different user groups, so you can do one each month, each week. There's a different one for machine learning, artificial intelligence, data science, things like that. You can get connected with different professors on campus that are doing things in data science. So there's a lot of different ways in which you can get involved while you're still here on campus as well. To build that credibility, build that skill set, you should get involved in different projects. For example, we have the Big Data Summit at the Research Park coming up. It’s free to register and just attend whatever talks you want to learn about a little bit more.
Q: How do people like me with a strong linguistic training, how do we succeed in industry, how we compete with people who actually come from more technical backgrounds? For panelists still working in linguistics-related jobs, how is your linguistic training contributing to your job and your role on your team?
Panelists: You have to trust your own instincts. It's up to you to figure out where your strengths are and to talk to those strengths. What are the things that come naturally to you and that you just excel at? And for me it was research. Think about those things for yourself and then start to tailor your story. It's not about altering what you've done because you've already done a breadth of things. It's about tailoring your message and tailoring your skill set to meet a specific need that may not be in the academic space.
Not all jobs involving research or data requires such a strong computational background. There are a lot of different types of positions out there.
I think you will be surprised, if you have linguistic background, how unique you are. Actually, you can contribute very unique perspective that computer science students don't ever have. I will give you a very simple example. For example, you would build a machine learning model but because of data compliance you cannot look at the data at all. So how do you build your model? You can’t see your data. But using linguistic knowledge, we know there are function words and content words, right? So now you can design things that bypassed the content words, but you still have the functional words and functional patterns come out to you. You can still learn a lot of things from the data.
My background was qualitative. I'm a sociolinguist. And I manage the tools that I have talked about. But more importantly, I know how to bring a team together that can do the work. And so as a linguist, you are able to bring together this ability to understand the individual, right? And that's where your skills needed to play out. You understand people and how to speak to people. What we learned as we learn big L language, and that is communication, that is the ability to speak, to translate, to do all of these things that everyone else struggles with. And so your skills, your ability to look at something and notice the subtle differences matter. Also, the number one thing I learned is that I learned how to learn, right? You learn how to learn something new and you learn how to learn something quickly. And so with your background with a PhD, you learn how to take something apart and evaluate it, what's wrong with this research. But then we would piece it back together and we would say, how would I make this better? That's how you advance the state of the art. It's the same thing in a business. You're looking at a problem, a business problem, you’d tear this apart and say, what's good, what's not good at? How do I make this better? All I want to say is, you can lead people by taking your linguistic skills, training others in them. And that's what you get as a PhD. You learn how to teach, you learn how to train, you learn how to coach, you learn how to bring people together, or how to project, manage to do all of these things are relevant and that it's a skill set that everybody in this workshop has. The question is, do you leverage it enough?
I think the other thing as a linguist, so often you're in a room with people with different backgrounds and you just need to know what does that mean? Or for you to explain the value of your research to somebody who doesn't have your background, who only has two minutes. But it really needs you, the expert to tell them what they're tested. How do you bridge that gap? Well, you have weekly experience sitting in the seminar, understanding people, viewing your cohort who don't do the same research. And they say, Well, what are you working on? And you're able to say it in just a few, a few words. That skill, that summary, people pay a lot of money for that these days. So again, think of these broad strokes skills. And as a PhD, you have a lot of experience, frequent experience presenting at conferences that are very valuable.
One other way that skills in linguistics can contribute to a team where such skills are in short supply is: to be an interpreter. Perhaps the team will encounter some language-related problem or some member of the group will ask a language-related question. You might know something about it right away (that's the instinct part -- trust it), but you also know how to read research work about language: you have the background knowledge to get through such scholarship in an efficient manner and then summarize and interpret it back to your team.
Q: So there are people like you who are linguistics-trained, and people who aren’t. So how do you guys work together?
Panelists: When you apply for jobs, people also look at how you collaborate with others. So that's the soft skill that you want to demonstrate and transfer that into your resume as well to see how you can collaborate with others. In the corporate environment, there are only very few times that only you alone do something or work on a product. You always work in a team or with other people, work together or to contribute to it. Every member in the team, they are unique so they can contribute their own part.
Q: What is an effective way of showcasing skills that were learned during an internship where you worked under an NDA or wrote proprietary code?
Panelists: You can talk about the details, but you can't really get into the specifics of it. If someone asked me, "Can you show me your code?" Actually, that's something that I can't disclose. But I can tell them a little bit about some of the libraries that I used. So you can walk them through it without actually having to show that to them. There are ways to get around this NDA without actually disclosing the proprietary information that you've signed an agreement to say you won't disclose.
Non-disclosure agreements are key fundamental tool in development of foreign language tests as well for reasons of security. I'm really glad this topic of NDAs came up because I don't recall it being in curricula anywhere in graduate school. There's, there's an overlap between NDAs, and intellectual property. IP often means something different in a university climate than out in a corporate climate. But that there are, there are areas within the university where IP is similar to what a corporate view of it is. Be clear on what that NDA is that you are signing. If you do sign something, there may be something that says you can't go work for a competitor company.
Q: What is your favorite networking technique?
Panelists: Nothing beats face-to-face. So let's pass this coronavirus and get back to that. I believe you should value everybody along your path, no matter where you are. Be kind to everybody around your path. I believe everybody you met along your path, it's not an accident. They're going to help you in your lifelong journey.