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Transferable skills and your resume

One of the hardest things about starting out on the Alt-Ac journey is understanding what skills you have that are desirable in other industries. A second, related concern is how to translate your experience from academia into the language used by professionals in those jobs. Though it may seem like you know nothing relevant for any jobs, you actually do! In this post I’ll try to list out as many such skills as I can think of, using my own experience both from grad school and as a precariously employed academic afterwards to formulate resume bullet points as examples.1

On terminology

Reading job ads is an art all its own. You’ll often see advice to use key words and phrases from job ads in your resume, and that’s good advice that I’ll try to use here. I’ve started collecting a list of relevant terminology in a separate post, which you can keep open as you read the sample bullets below or as you peruse job ads.

On resumes

This is not a dedicated resume post. I do want to point out some key ingredients I’ll stick to:

Transferable skills with sample resume bullets

Ok, hang in there, this will be long!

The most important point I want to stress before we get to the details is this: in a non-academic job search, the actual content of your research matters less than the skills you learned along the way. For the most part, there will be no need to describe the details of your dissertation or other research, and you should avoid field-specific jargon; rather, what the reader is interested in is how you can apply the same skills to the problems that they want to solve.

1. Qualitative and quantitative methods; Experimental design, data analysis

Experimental design, data analysis, qualitative and quantitative methods, and other variations on this theme are by far the most common answer I get when I ask other former academics what skills from grad school they find most useful and commonly used in their new career.

There are, in fact, various skills involved here, from “algorithmic thinking” (how do you think through a process, how do you break down a problem in a way that an engineer could then code?), to picking the right methodology for your problem (both at the design and analysis stages), to implementing the experiment, to data visualization and analysis. How do you identify problems to study in the first place and how do you operationalize them? How would you interpret a given p-value or finding? How do you distill the main insights and present them to others?

I was lucky enough to engage in several experimental collaborations while I was in grad school, so here is an attempt to tell some of those stories. (I’m focusing here mostly on the data collection part, not the writing up of the results part, but see more below):

(The experimental chapter of my dissertation on multiple wh-questions and intervention effects, aka chapter 8 of my monograph:)

“Designed and conducted 15 behavioral experiments and tested 500+ participants to study the structure and meaning of complex questions in English; wrote design documents2 and guidelines; analyzed the results using linear and logistic mixed effects models in R.”

(My first foray into experimental work was through a study of English ‘most’ and ‘more than half’ in the MIT Experimental Syntax/Semantics Lab. It also led to my first Generals Paper:)

“Developed and implemented over a dozen studies of quantified expressions in English using diverse methodologies, including self-paced counting, eye-tracking, picture-matching, picture selection, covered box, and grammaticality surveys. Analyzed the results using mixed effects models and parametric and non-parametric tests in R.”

(My one and only attempt to do first language acquisition. Working with kids is hard!)

“Collaborated with experts to study children’s understanding of quantified statements at different ages; collected data from 74 English-speaking children from Boston area daycares and afterschool programs and at the Boston Children’s Museum. Analyzed results using parametric tests in R.”

(In 2012-2013 there was an opportunity to have free access to a new combined MEG/EEG machine at the McGovern Institute at MIT BCS. The bullet below describes one of the two studies I participated in; here I was involved in study design and data collection but not in the analysis, which required expertise I never developed.)

“Designed and executed a study of complex word formation in English using MEG and EEG measures; collaborated with a research group in Japan to compare findings in English and Japanese to adjudicate between two competing theories of morphology.”

Studies that use Praat, computational modeling experiments, artificial language learning, simulations, and all manner of other methodologies would go here too.

Not all jobs would require getting into this much detail or having this kind of focus on the methodology. Below you’ll find other bullets that refer to some of the same projects but highlight different aspects.

2. Human data collection methods

As a linguist, you may engage in several forms of data collection from humans which are also used in industry in similar ways. One such method is using in-lab or online studies on Amazon Mechanical Turk or similar platforms where you publish projects for participants to work on. The other is using various forms of interviews, one-on-one or in groups, structured or semi-structured, usually in the form of consultant work (aka fieldwork) with native speakers of a language of interest.

(This describes the first two projects from above plus various others, but has a different focus:)3

“Recruited >2500 participants for over 3 dozen studies on Amazon Mechanical Turk and >200 participants for in-lab experiments over 4 years; implemented methods to detect cheating or distracted behavior to ensure data quality and reliability.”

(My consultant work was pretty limited compared to some other linguists and never involved travel to a field site, but nonetheless I did engage in it for several projects:)

“Conducted semi-structured interviews with native speakers of low-resource languages (Tibetan, Kaqchikel, Chuj) over 3 years to study grammatical properties of these languages, resulting in 3 papers and 5 presentations.”

3. Large-scale data collection; generalization; labeling, annotation

Linguists also engage in language data collection and labeling, although we don’t tend to think of it that way. Along the way, we may also create annotation guidelines — that is, documentation surrounding how to identify the properties of interest in our data, including what values a property can have and how do decide what (if any) value to assign to a given datapoint.

On a related note, a superpower linguists have but tend not to value nearly enough is the ability to generalize from a large set of unstructured data. More precisely: the ability to construct the data set, adopt a set of properties that best capture the variation in the data, find the generalizations and identify the exceptions, and draw conclusions about how to best explain both. This is such an important skill!

(This describes a project on gender representation in syntax example sentences in published linguistics papers:)

“Led a team of 3 graduate researchers and 24 undergraduate research assistants in a large-scale data collection project; created a corpus of >22k example sentences from 900+ journal papers published in leading linguistics journals over 20 years. Created annotation guidelines to label the data for 10 properties. Oversaw data analysis using non-parametric tests and sentiment analysis using the Bing and NRC methods in R.”

(Consultant/field work obviously involves data collection. Really, all linguistic work involves some form of data collection, I think.)

“Constructed a corpus of over 1,000 example sentences elicited from a native speaker of Chuj (Mayan) over 2 years to study the properties and uses of question words (e.g. ‘who’, ‘what’) in the language.”4

(Although it’s a bit harder, you could do the same thing for a fully theoretical project, as well. Here’s a description of what was my first-year independent study project and eventually became my second Generals Paper and an early publication.)

“Synthesized >500 data points5 from Hebrew, English, and German over 2 years to motivate a new theory of how complex questions are formed.”

I could do a version of this kind of accounting for the data in my dissertation and monograph, too, for example. One easy thing you can imagine doing to facilitate this bean-counting is to actually create a corpus of all your data that you can then also independently post on your website, leading to a separate deliverable you can claim in your resume and link to. It’ll also help you be precise about the labeling part of a bullet point: what properties are you looking at and what values can they have? those are the features you’re annotating for!

4. End-to-End responsibility; sharing findings in written form

These two are separate skills, but you can usually showcase them in one bullet. What I mean here is taking a large undefined space, identifying an interesting problem, and following through to completion by proposing a new solution, including writing it up and presenting it. If you’ve written any original paper (squib, qualifying paper, thesis), you’ve done this!

The ability to teach yourself new things, navigate an uncharted space without getting overwhelmed,6 and carving out a meaningful and doable project within this space, is an incredibly valuable skill you learn in grad school.

(A bullet that turns 2/3s of my academic CV into one line |lolsob| :)

“Led cutting-edge collaborative linguistic research at R1 universities, shepherding projects from ideation to dissemination, resulting in: 1 monograph, 1 edited volume, 20 peer-reviewed articles, 23 proceedings papers, and over 100 presentations.”

(A bullet that focuses on my dissertation work:)

“Led all aspects of a multi-year research project on the structure and meaning of questions in typologically diverse languages, proposing a solution to a long-standing problem going back to the 1970s. Secured and managed external grant funding. Designed and analyzed data from 15 experiments, testing 500+ participants. Published 1 monograph and 7 papers, and gave 24 talks at conferences and colloquia.”

5. Innovative problem-solving

Again, anyone in a graduate program has done some of this. It’s easy to overlook this (and many other skills on this list) because everyone around you also has done the same. But out there in the ‘real world’ these skills are rare and worth celebrating.

(This is a project I did in grad school that initially came about because I needed some help implementing my own experiments. Eventually I also got a small internal grant and co-taught a workshop on it:)

“Co-created Turktools, a free toolkit for implementing and analyzing behavioral language experiments on Amazon Mechanical Turk, including HTML and R scripts for data visualization and analysis. The tools have powered several dozen studies world wide and are still actively used today.”7

I’d argue that the bullet immediately above this section which discusses my dissertation work also demonstrates innovative problem solving! Turns out that it’s kind of hard to write bullets that only demonstrate one skill without also showcasing others.

6. Securing buy-in from stakeholders

Getting grants is probably the most direct way you can show you’ve not only done interesting research, but also convinced others it was worth investing in.

(I’ve mostly had small grants and fellowship but one large award is helping me out here:)

“Justified research value by obtaining >10 grants and awards totaling >$120K.”

7. Communication and teaching

This may sound cliche, but being able to communicate complex findings to a range of audiences is not something everyone can do. This is the second most common skill my former academic friends tend to mention as something useful they learned in grad school.

(Presentation focus:)

“Communicated research insights to diverse audiences in >120 talks and panels at national and international conferences, colloquia, and public outreach events (with audiences averaging 50-100 participants per event); gave interviews for podcasts and social media outreach campaigns; wrote a blog post series to showcase non-academic career paths for linguists.”

(Teaching focus:)

“Developed and taught 14 courses in linguistics at 3 universities; designed curricula, lecture notes, assignments, and assessments. Managed 14 teaching assistants and over 500 enrolled students.”8

(Focusing on one particular course:)

“Proposed and designed all aspects of a new freshman seminar ‘Illusions Of Language’ in the Yale Linguistics department, including lecture notes, readings, activities, assignments, and assessments, leading to the second-highest enrollment numbers in the department and forging a new pathway into the linguistics major. Taught 2 iterations of the course, and passed the notes on to 3 new instructors, as the department has adopted the seminar into its regular course offering.”

The above bullets stay at a high level. If you’re applying for a job in EdTech or SciComm, e.g. think of curriculum editor or instructional designer, you’ll want to get a lot more specific about the courses you designed, the assignments, the assessments, the readings, etc. Maybe you’ll want to talk about your teaching evaluations or enrollment rates. Maybe you want to talk about mentoring TAs and providing professional development advice.

Generally, developing assessments and providing feedback to others on their work also exemplify project management and leadership skills. In some contexts you’ll want to showcase these skills more explicitly in bullets like the above.

8. Leadership

Academic work can be isolating and lonely, but there are still opportunities to be a leader.

(A project-focused version:)

“Hired a team of 24 Yale University undergraduate research assistants for a large-scale data collection project. Oversaw the creation of a corpus of >22K datapoints used to analyze gender representation in linguistics. Provided training and ongoing feedback, managed and approved budgets, and supervised quality assurance of the results.”

(A teaching-focused version:)

“Directed teams of 2-3 teaching assistants in high-enrollment linguistics introductory courses. Provided mentorship to develop assistants’ professionalization, teaching skills, and ability to give students constructive feedback.”

(An advising version:)

“Supervised the research of 6 students on undergraduate theses, PhD qualifying papers, and PhD dissertations. Provided ongoing feedback on research findings and directions, ideas, and writing. Aided in professionalization and mid- and long-term career planning and growth.”

(Committee leadership version:)

“Chaired the Linguistic Society of America (LSA) Committee on Gender Equity in Linguistics. Co-supervised the production of The Resources on Equity and Inclusivity in Linguistics guidebook and 23 Pop-Up Mentoring Events and led a large-scale data collection effort. Co-won the LSA Service award in 2019. Provided ongoing progress reports to LSA executive leadership and engaged in participant and leadership recruitment within the committee.”

(Something I might have written in grad school:)

“Led a team of 3 linguists in a grant-funded study of quantified expressions in English. Oversaw >10 experimental studies using diverse behavioral methodologies, leading to 3 peer-reviewed articles and 13 talks at conferences and colloquia.”

Leadership experience can be harder to come by as a student, but there are various ways to get it. I can think of some other additions I could have tried to spell out here from my student days — for example:

This kind of experience can also come from other sources, like from any volunteer experience or other groups you belong to. It doesn’t all have to be related to academia.

9. Project management

This is experience with things like managing timelines and budgets, getting roles staffed, making sure everyone has the tools they need to do their job. Also recruiting participants for experiments, wrangling speakers for a talk series, organizing events, booking rooms and getting food for an event, writing sponsorship letters for visitors who need visas, etc.

If you are someone who does fieldwork, the admin stuff you do to organize a research trip or to buy supplies, or even to get IRB approval for your work, can all go under this heading.

(Organizing conferences:)

“Co-organized 5 national and international academic conferences and workshops with >100 presentations and >1000 attendees. Secured budgets, provided visa support for international visitors, and oversaw talk selection and programming.”

(Broader version:)

“Recruited >2500 participants for 30+ in-lab and online experimental studies. Hired and supervised 20+ research assistants, mentored 14 teaching assistants, and advised 15+ students on ongoing research projects. Organized dozens of conferences, workshops, reading groups, and lab meetings. Obtained and managed research budgets.”


Well damn, I wish I had done this exercise when I was first on the job market. I could have had a much stronger resume than I did (although I did quite alright, no complaints).

Many of the bullets I’ve written here describe different aspects of the same project. If I were actually using them in a resume, I might combine parts of 2-3 of them into one to showcase all the different aspects of a project at once, and I’d probably try to be more concise overall. And, of course, what I’d actually pick to include and what I’d focus on would depend on the job I’m applying for, what the job ad says, and what I otherwise know would be important.

The resume is not a list of everything you’ve ever done. That said, you can and should keep a longer list of bullets in a master resume which you can update and pick from as needed. Doing this, along with some notion of what kinds of jobs you’re interested in, can also help you work backwards to detect areas where your background may be lacking, so you can focus on creating the opportunities for yourself to gain the needed experience. I would strongly advise this exercise of walking through your academic CV and experiences and writing them up in resume bullet format to everyone.



  1. To be clear, I had none of these bullets on my resume when I was first looking for a job! I did quite well regardless, but thinking back, I could have had a much stronger resume. 

  2. You can think of handouts or slides you’ve written to present to e.g. your advisor or lab peers, which describe the design, aim, and potential outcomes of your results along with a plan to analyze them, as a design document, which is a common business term. You might consider collecting some of those, cleaning them up if needed, and making them available as samples on your website when you apply with links in your resume. (I did this when I started applying.) Likewise if you’ve ever worked with anyone else to process your collected data, and sometimes just as good documentation practice, you might also have something you might consider an annotation guideline, which would describe what values your features of interest can have and how you determine them. 

  3. A massive thanks to 10-years-ago-me for downloading a spreadsheet with all this information from AMT, because having to manually count up all the participants in the various projects I’ve been involved in over the years would have been somewhere between tedious and impossible. This is most definitely an undercount: the spreadsheet leaves out my entire dissertation year, where I estimate I ran easily over 500 participants for my experiments. Lesson for you, dear reader: document these things now! It’ll be infinitely more difficult the more time passes by. 

  4. Sigh, my notes are organized into 25 files by date. I counted 100 examples in the first file and 71 in the second but I definitely didn’t feel like counting everything carefully so this is just my conservative estimate. See again my advice in the note above! 

  5. Honestly I think this is a massive undercount. I would like to once again thank past-me for holding on to all my notes and handouts from grad school but there are so many of them and I don’t feel like opening them all up and counting all the examples I collected and tested over the 2ish years I worked on this project, since I’ll never use this particular bullet point outside this post. 

  6. Or rather, being able to collect yourself and keep going despite feeling overwhelmed and still making it through! 

  7. This is another one of those ‘hard to quantify’ things, especially if people don’t cite our paper. (Please cite our paper if you’re using our tools!) I stopped keeping track of it a few years ago. 

  8. Oh how I wish I had done a better job keeping track of enrollments. Are you sensing a pattern here, yet? 

  9. It used to be that there was a Spring phonology workshop and a Fall syntax/semantics workshop, which you took in your 4th and 5th semesters respectively, with your cohort only. What if you didn’t want to write a phonology paper in your 4th semester? (or at all!) Too bad. Now there’s just a Spring and a Fall workshop with no topic limitation and no cohort limitation. I think that’s just all around better. You’re welcome, MIT students. (To be clear, I definitely did not do this on my own; but I was an active member of the group that investigated this and made the recommendations and decisions, and I think that counts.)