How I decided to leave my academic job

The shortest and simplest explanation is that I left my academic job at OU to prioritize my personal life, and this is how I described it at first. I was in a long-distance relationship for the entirety of my time at OU, and my partner and I were ready to share a home base. But that explanation is too brief— it took years for me to reach this decision, and I didn’t make it lightly. Inspired by some friends who have recently shared more details about their departures, I decided to share a longer version of my story.

I want to acknowledge up top that I had a decent savings by the time I left (Thank you, Oklahoma!), another financial safety net for the job transition (my partner), and no family to take care of or other big responsibilities (Cookie is sooo tiny!). All of this released a lot of worry and uncertainty around my decision. I’m grateful for that, and I wish that everybody could choose what’s right for them without having to worry about how to make ends meet.

Settling into the field in graduate school

To be honest, there were indicators during my academic career that I wasn’t all-in. When I decided to attend UCSC, I specifically opted for on-campus housing so that if I decided to leave the program, there would be no penalties for breaking my lease. In my first year, I felt insecure about my level of dedication. I can’t really access what I was feeling at the time (it was, of course, FIFTEEN DANG YEARS DANG AGO), but it was something to the effect of “My life does not revolve around this.” I liked linguistics, but I liked other things, too! A new friend, who was in the MA program at UCSC and was planning to leave the field, used the phrase “I want to be a body, not a brain!” to describe their feelings, and I thought, “Me too! I’m also a body in this world!” I brought this up at a social event among grad students in a “Don’t we all feel this way?” kind of way, and I was surprised by the response. Apparently, we did not all feel this way. But I stayed in the program (I like linguistics!) and continued to try my hardest to do well.

By the time I started to work on concord (in my third year), I had really settled into things. It was exciting to feel like I was breaking new ground, and even though I think I probably cried about once a week (lol), I recall enjoying my life and enjoying my work. When it came time to apply for jobs, it did not even cross my mind to go into industry, even though there were many recent graduates from my program who had found gainful employment in the tech industry. I was certain that I had to try to be a professor even as I was uncertain if it was what I wanted. In fact, I didn’t really consider asking my partner to move with me to Oklahoma, and one of the reasons was that I wasn’t sure if I wanted to be a professor for the rest of my life.

Add “decide your whole life” to my pre-tenure expectations

When I was getting ready to head to OU, my partner’s career was also starting to go in new directions, and we mutually decided to let each other follow these career paths without the pressure of making a decision. I spoke with faculty at OU and at other departments who would console me by saying things like, “Oh, such-and-such faculty member lives far away from their partner, and they make it work!” and I thought, “But this is not something you HAVE to deal with in life! And it is not what I want!” I knew, at least, that sacrificing sharing a home base with my partner was not something I was willing to do forever.

Maybe because of that, I put a lot of pressure on myself to figure out if being a professor was what I wanted in those early years. Asking my partner to give up his career to move to Oklahoma (or somewhere else, if I got a job elsewhere) was a big deal to me, and I wanted to be sure. But of course, I had never been sure (you know, like sure-sure). And furthermore, my early years at OU were difficult because I was having trouble getting work through prepublication peer review (which is a bad practice). It felt impossible to decide if this was truly what I wanted when I was also struggling to feel successful. 

Over winter break in my second year, I had a panic attack while visiting my partner, precipitated by stress/anxiety about everything I needed to do for work. This was an important signal, as I have had maybe three panic attacks my entire life. I decided I should start seeing a therapist again.

One of the early revelations that came out of therapy was that I was (i) not fully committed to my position at OU (because I was “trying to figure out” if it was what I wanted) but (ii) acting in my role as though I was fully committed in terms of effort expended and expectations I had for myself. This made it very difficult to feel calm and secure, so I decided that I had to commit to my faculty position and my life in Norman as long as I was there. I bought a house at the end of my second year (a house I loved dearly; Thank you, Oklahoma!), and I released myself from the pressure of needing to figure it out. The next several semesters were generally pretty fun, I had some more success, and I liked my job.

“Wondering whether” can be an answer

In roughly the middle of my fourth year, I was still wondering if it was what I wanted. I realized that if I was still wondering after 3.5 years, that was enough of an answer. I also did the math and realized that I had 30+ working years left (Thank you, American capitalism!), which is plenty of time to build a second career. I liked my job enough, but I didn’t feel like it was my calling or passion, and I knew what I was sacrificing in order to pursue it. I began to think that it was worth it for me to try to find something else that either (i) I liked even more or (ii) did not ask me (or my loved ones!) to sacrifice so much. 

After a particularly stimulating academic conference, I reconsidered the decision for the final time: Did I want to leave Academia entirely, or did I just need to leave my job at OU for a “better” academic job? Another friend who had left for industry reminded me in a helpful phone conversation that the academic job that I could be happy with over the long term was extremely difficult to get (if it even existed). For years, I had held on to the promise of an academic job where (i) I was treated well by colleagues and the institution (and that includes pay and research funding), (ii) I could research and teach what I wanted, (iii) I had dedicated and funded graduate students, and (iv) I lived in a place I wanted to live. But how many of those jobs are there? So many faculty members sacrifice some number of these things in order to be faculty. I had been at an institution where I was sacrificing to some degree on all four of those qualities, and though there were things about my work that I liked, I knew that they were not enough to endure those sacrifices indefinitely.

It would take time to find a new way to make money, but I had over 30 years to build a new career. I resolved to go for it.

What do I think about it now?

While my relationship wasn’t the reason I left academia, it was the catalyst for my decision. It forced me to wrestle with issues much quicker than I might have otherwise, but I ultimately arrived at the right decision for me. Even if another academic job existed in San Francisco, where my partner and I planned to live together, I knew that I wouldn’t apply for it. (And I still wouldn’t.)

Folks sometimes ask if I miss it, and the answer is yes and no (but mostly no)! I say this every time I talk about it, but I’m so glad that I spent 11 years of my working life getting paid to study generative linguistic theory and teach linguistics. Human language is one of the great loves of my life, and I find it incredibly fascinating and fun. I do not regret my decision to take the job at OU, I don’t regret my decision to stay for five years, and I don’t regret my decision to leave. I have said before that I don’t miss the amount of research I used to do, but I also don’t really miss teaching. That doesn’t mean I didn’t like it! It just means that there are so many things in life to be enjoyed, I found other things to enjoy, and it was enough to fill my cup. I treasure the good relationships with students and faculty I made while I was at OU, but I definitely do not miss grading papers. So it’s “yes!” because I remember it with fondness, but “No!” because I don’t regret leaving, and I don’t yearn for the work.

I was so worried about what the rest of my life would look like if it didn’t revolve around linguistics, teaching, and the broader linguistics and higher education community, but it’s okay. I still have friends from that time, and I still engage with linguistics research from time to time. But now, it’s entirely on my terms.

(This is the third post in a series of blog posts about my transition from academia to industry and my feelings about my time as an academic teacher and researcher. To read more, see the “industry transition” tag.)

Reflections on research after academic jobs

When I left the job where I was paid to do linguistic research (among other things), I told myself that I was going to continue to do research (i) as long as I had time for it and (ii) as long as I found it fun (or you know, as long as I wanted to). I anticipated that within 2-5 years, the sun would set on my ability to contribute new research to generative and typological linguistics. I left my academic job just about 2.5 years ago (in May 2019), and I have been working in industry for almost 11 months. As I continue to develop roots in this next phase of my working life, these thoughts have been creeping back into my mind.

Between deciding to leave and actually leaving: anticipatory grieving

Some post-academic folks I speak to seem to have little love lost over their academic research interests, but this was not me. I felt very sad about saying goodbye to those things. In particular, I recall feeling saddest about the changing relationship to Estonian, my primary research language. Leaving my academic post meant saying goodbye to biannual trips to Estonia (for fieldwork and swimming), and it meant less occasion to contact the friends who taught me about their language. I might say that I was worried I would miss Estonian and Estonia so much that I would regret my choice to leave my academic job, but really I think I was just sad that things were going to change.

In the middle: sometimes a source of comfort and purpose and sometimes a distraction

My transition to industry took longer than I expected: 14 months after I relocated to SF (11-12 of those spent actually searching), I got my first industry offer. I have shared this before, but it bears repeating (just so people know): I experienced some of the lowest/darkest moments of my life during that time. Searching for jobs ALWAYS sucks, and so does trying to make a career transition. During this period, doing a little bit of linguistic research would help temper the feeling of purposelessness I often felt. For example, for a few months, I had a weekly reading group with Ruth Kramer, who has been both a dear friend and research advisor essentially since we met in 2008. I spent maybe 20-30% of my “working time” doing linguistics, just because it gave me something to do that I felt like I knew how to do.

There were other times where doing linguistics felt like an indulgence. It felt like it wasn’t quite the thing I was “supposed to do” in order to make myself more competitive for a job. In retrospect, it wasn’t that doing linguistics was EITHER helpful for me or a distraction; it’s that sometimes I needed it, and sometimes I didn’t.

Now completely in industry: I liked it then and I like it still, but I don’t regret my choice

I’m now just over 6 months into my first permanent position at industry, working on problems I find interesting with a team of people I truly enjoy. I did actually have some research output over the last 12 months, and (to my surprise, honestly) I was recently invited to give a colloquium and contribute to another handbook, so I think it’s clear that I’m still doing research at this point. BUT GOODNESS, it’s even harder to make time for it now! After I wrapped up my joint paper with Kyle Mahowald and Dan Jurafsky, I didn’t have any research deadlines, and weeks without doing linguistics passed by before I realized. It’s not that I no longer enjoy it, it’s just that it’s one of many things I enjoy that I have to use time outside of work to enjoy.

This made me think about how I felt before I left: would I miss it? Would I be sad about letting these things go? At this point in my post-academic life, it seems the answer is “No.” That could be because I’ve let go gradually. It could also be because I still haven’t completely let go— I have a handbook chapter that is still set to come out (handbooks are… slow) and I was just asked to contribute to a different handbook (hello, deadline). But I think a large part of it is (i) I’ve had space to move on and (ii) I have a new career that is providing plenty of intellectual stimulation. AGAIN, I must stress that this doesn’t mean I didn’t like it then or don’t like it anymore! I’m still very happy I spent 11 years of my working life dedicated to linguistics teaching and research. I’m also happy about learning to do new language-related things!

Future: What’s actually worth my investment? Can I walk away from unanswered questions?

Since leaving, I have realized that even if I continue to do theoretical/typological research when it is no longer part of my job description, it will not look the same as it did before. There were many research-related activities I did when I was a professor:

  • Read theoretical papers: both to stay current and to try to find inspiration when solving a particular puzzle
  • Write papers: to share knowledge and proposals in a permanent form
  • Present at conferences: to share knowledge and proposals
  • Give invited talks: both colloquia and working group talks
  • Review articles: if I’m going to keep writing, I should keep reviewing

This is a lot of tasks! And realistically, on a busy research week, I probably can spend about 5 hours on this. Deadlines have become significantly more motivating than they were in the past. I have been able to complete necessary work and not much else. For example, I have had to be more selective about reviewing, and I barely read enough to support my own projects. Forget about staying current!

At some point, it will be time to effectively stop. I have a pipe dream of writing a book and just making it available, published or not. There are too many things I’ve learned—especially about concord—to just leave them in my brain. There are questions that I want to know the answers to, and if I don’t find the answers to these questions, then I don’t get to know what they are (because either nobody else will, or they they won’t tell me if they do). Trying to get all of my knowledge on paper is one way to possibly avoid that, but I also think I will have to leave some of these questions unanswered. I suppose that’s also just part of moving on from jobs more generally— letting go of in-progress work.

What I (a linguist) did while searching for a job (in tech)

This is a post about (or the first post in a series about) the search for my first job in the language + tech industry space. I perhaps could have called this something like What I Did to Get a Job, but one of my opinions about getting your first industry job is there is no silver bullet for the task, so I can’t say that any of these things were necessary in getting my first job. However, I did some things while searching for a job and then did manage to get a job (first a contract job and then a full-time job).

To say that again another way:  there is no one thing that you must do to be able to get a job nor is there one thing that will enable you to easily find a job if you do that one thing. Unfortunately! So what you should do is just keep trying to develop.


What I did to learn/practice coding (mostly Python):

  • Free intro course (keyword FREE); I did Codecademy‘s Python 2 course, because their Python 3 course costs money.
    • There are differences between Python v2 and v3 but they are minor and you will pick them up.
    • This will help you learn the basic data structures.
  • Automate the boring stuff is a good book, and all the chapters are available online (just scroll down the linked page):
    • Learn to manipulate files (reading/writing CSVs, text files, and JSONs): Chapters 9, 13, 14, 16 (at least!)
  • Free mini-courses on Kaggle
    • They have great bite-sized courses on a variety of topics, e.g., Intro to Python, Pandas, intro to ML, advanced ML, data cleaning
    • When you’re done, you can even add a certificate to your LinkedIn profile, which you should do!
  • NLP-focused content
    • Introduction to SpaCy (great library for NLP)
    • NLTK book
    • My two cents: don’t worry about the syntactic details; focus on internalizing the steps of an NLP pipeline in a broad sense
      • I got overly concerned with knowing this well—I never really got there, and I also haven’t had to know it well for either of my jobs or for my interviews.
      • The file management stuff is more important!

The main thing I have done at my jobs with Python is file and data management. For file management, learn to do things like (i) list all the files in a directory, (ii) perform the same operation on every file in a directory (or just every .csv or .txt file…), etc. For data management, you want to be able to manipulate different file types; I would say focus on being able to import and export csvs and jsons. In my opinion, you should prioritize learning (some of) the pandas library, because it’s powerful and also used by data scientists. I have some aspects of pandas memorized by now, but I still look things up all the time. That being said, I once had a coding interview where the code was written with the csv package, and I couldn’t remember the syntax. I just tried to do my best in the interview, but I did feel afterwards like I should make sure to remember how the csv package works. For language work, the csv package is also probably quite a workable solution— some of my colleagues at both jobs have used csv instead of pandas.

Learning ML/AI/NLP

The key thing here is that you need to focus on being conversant in these concepts rather than necessarily being able to write them. Maybe you can get to that, too, but step one is understanding what the pieces are. The reason to watch and read these things is not so you can necessarily do this work (speaking for myself, I do not build ML models at work), but so you know enough about them to know kind of how the enterprise works. To be clear, there are some “linguist” jobs where you build models, so if you’re interested in it, it’s definitely worthwhile. However, you can secure a job without building a model. Again, these are the things that I read or watched while I was searching, not necessarily “must watch” or “must read” sources!

We did not get to see the models in my job at Amazon. I still think it was helpful for me to know a little bit how these programs work so I could talk/think about how the data would be used. 

“Do a project”

A lot of people told me some version of “do a project.” If your feeling when you hear that is “Sounds good, but what?” I don’t blame you! As someone offering advice, it is easy to think that project ideas abound, because once you start working, so many more ideas come to you. However, when I was searching, I just didn’t know what projects I could do. Most identifiable NLP projects (i.e., papers) are multi-authored, so I felt that I couldn’t make headway there (let alone the fact that I was still learning!). I definitely didn’t think I was doing any projects that were big enough to count as projects (whatever that means), and at times, I felt like I couldn’t even if I wanted to.

Surprise! I actually did some projects

Now that I’ve been working about 8 months, I can see that I did “do a project” a few times. Here is a reasonably complete list.

  • Poketext: scraped Pokédex entries from Bulbapedia pages and saved it in one giant text file
    • When I started this, I had in mind that I was building a corpus. I don’t know what the corpus would be used for— probably a good idea to have an answer to that.
    • This gave me some experience web scraping with the Python module called BeautifulSoup
  • Poketext (part deux): try to fix issues in some of the sentences that I saw using SpaCy
    • Some of the sentences in the Pokédex would refer to the Pokémon by name, but some would just use a pronoun or an NP like “this Pokémon”.
    • I created an algorithm to replace the pronouns/NPs with the name of the Pokémon, and I encountered some interesting issues in the process.
  • Concord: I converted my typological database from a somewhat unwieldy spreadsheet to a format that was more computationally friendly and easily updated.
    • Each individual language in the data set is one JSON file
    • I wrote scripts to import all the JSON files from the directory and output descriptive stats about the data set
    • I wrote a program that creates a data file for any language that is new to the data set (after I’ve documented it myself). It asks me a few questions about the language and then creates the JSON file.
    • Because I did this, Kyle Mahowald found my data and we started a research collaboration.
    • FWIW: this is the project that ended up being the most useful (imo) for getting my first job.
  • Estonian spell checker: Messed with some Estonian data to adapt Peter Norvig’s very simple English spell checker for Estonian.
  • My blog!: Though I do sometimes write about esoteric issues connected to my linguistic research, I also tried to write some posts addressing language + data in a friendly, accessible way.

My recommendation is that you put these projects on a github and/or on a website, no matter how small you think they are. If recruiters or hiring managers get curious enough about you to look for your web presence, you want to give them something to chew on. Projects that are representative of your current skills make the most sense—if they’re not flashy enough for a particular job, then trust me: you don’t want that job (right now)!

Additional project ideas

Feel free to riff on any of the project ideas stated above. If none of those sound fun to you, here are some ideas to inspire any do-a-projects you might pursue.

  • Build a test set for an imaginary classifier: Test sets are smaller than training sets, so it won’t take you as long to annotate them.
    • Columns (one idea): label/annotation, URL, first paragraph, first paragraph tokenized or split on spaces (eg, using SPLIT() in Sheets)
    • Classifier ideas:
      • X or not X: news articles that are or are not about a certain thing (eg, about accidents or not, about the stock market or not, or even subjective categories)
      • native vs non-native (or: native vs. translated): sentences/paragraphs that are or are not from native speakers.
  • Build a corpus with web data (possibly scraping with BeautifulSoup if you can’t find the data more easily): collect examples with the idea that a person could design an annotation using the data you’ve collected.
    • There are lots of “corpus of movie review” tutorials online— these won’t “make you stand out”, but you will learn a lot from doing them.
    • I also saw something that involved pulling all the dialogue from a TV show and using that as data
    • Could use this corpus to feed into a project like the one above.
  • Playing with old research data: If you have any research data that can be put into spreadsheet format, try to do something with it in python.
    • Maybe that’s data visualization,
    • maybe that’s creating randomized samples of the data,
    • maybe that’s writing a script that would let you add to the data (e.g., what fields does the script need to ask for),
    • maybe you want to create a database of linguistic examples that you have gathered in your fieldwork/research and tag it for relevant information (eg, these are my relative clause examples, these are my wh-question examples, …)
    • If you don’t have any spreadsheet data from your research, you could download some samples from and play with those. Eg, can you figure out how to download 3 samples from WALS and combine them all into one file (so that if a language appears multiple times, you consolidate all of its values into one row?). This stuff isn’t super hard to learn!
    • just do something to give yourself something to work on and slowly figure out

The important thing to remember: there’s no secret beyond trying

Unfortunately, if you don’t already have computational or statistical skills, it can be hard to show a recruiter that you can contribute. There is no secret to this task— you just have to maintain your ability to try new things and keep waiting for a little bit of luck. I don’t mean this in a hokey All You Need to Do Is Try kind of way. I just mean it’s probably not helpful to worry about whether you have done The One Thing You’re Supposed to Do. Such a thing probably doesn’t exist. When I wasn’t worried about that possibility, I kept working on projects as long as I found them fun/interesting/whatever, and when I stopped feeling excited about them, I moved on to something else. I never did finish that ML course even though I told myself I would. It’s okay— just keep trying to practice or learn.