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I recently started a full-time position at the Humane and Sustainable Food Lab, and I’ve been reflecting on how convoluted and indirect my path was. I thought that journey might be worth sharing.

In the genre of "Well, how did I get here?," I appreciate Johannes Haushofer's CV of failures because it helps correct selection bias in career stories. If we only see the things that go right and the outcomes that emerge from them, we'll have a truncated sense of what leads to what. So here's my story.

Stage 1 (2006-2010): Aiming to be a political science professor

I went to a small US college whose graduates are overrepresented in EA and in PhD programs. I majored in political science,  where I found the work reasonably enjoyable and easy.[1] Most of my friends ended up getting PhDs and were pretty serious academically, and I chose the same career path: a classic case of peer effects.

Stage 2 (2010-2013): Trying other paths for a few years

As a senior in college, I had an intuition that 21 was a bit young to start a PhD, so I did other stuff for a while:

  • An Americorps program where I worked as a teacher's aide in a kindergarten classroom in D.C.
  • Taught English in Thailand to middle and high schoolers
  • A two-semester internship at a think tank at which I produced approximately zero output. 

I wanted to see if any job seemed like a better fit than"professor at Swarthmore/Middlebury/Pomona/etc.," but nothing seemed more compelling, so I applied to PhD programs in fall 2012[2] and enrolled in one the following year.

Stage 3 (2013-2015): Grad school is not a good fit

My first year in graduate school -- again as Timothy Burke would have predicted-- was very challenging and not at all like college. I took survey courses with giants in the field and was bored senseless. The required stats classes were total drink-from-the-firehose experiences. I thought I was picking up enough to get by, but I wasn't, a fact I was alerted to when I got a letter from the department chair saying that my academic performance was not meeting expectations. So I wouldn't say grad school went very well.

I did however, fall in with a dyed-in-the-wool experimentalist as my advisor who I really like and with whom I'm still friends. I took a few classes with him and we had some projects I was excited about. However, when people in the department looked at these projects, they often asked: how does this fit into our discipline? 

At the end of my second year, I failed my comprehensive exams in American Politics. At the beginning of what would have been my third year, I failed them again, this time in both American and Comparative politics. I just wasn't cut out to be a political scientist, and I was told to leave the program and venture into the real world. (I got a consolation M.A.)

Stage 4 (2016-2017): Transitioning to tech 

This was a difficult period in my life. My first job, at a well-regarded international development NGO, fell apart after a few months.[3] At that point, I felt like a total failure, like no job would ever work out. 

One morning in spring 2016, I ran with my advisor and explained the situation, and he offered to pay me for the summer to work on a project that we had started in grad school: a meta-analysis of intergroup contact experiments. We did that and ultimately published the paper in 2018

Meanwhile, I tried to get a job in tech, because a friend from HS had said that that was where the action was. I got an unpaid internship at a friend's startup in fall 2016 and a paid gig practicing English conversation with Japanese employees of a big bank. I checked Hacker News every day and applied to a job posting there to be a junior developer advocate, a title I'd never previously heard of, at a startup. I started that position in March 2017.

Stage 5 (2017-2021): The tech years

I worked at that company until early March 2020. Towards the end, I knew things weren't going to work out,[4] and I looked for an offramp. I ended up working with my former co-authors as a research assistant on a meta-analysis they were working on. I also moved back in with my parents in the suburbs and, when not working on the meta-analysis, collected unemployment. I was 31 at the time.

As that project winded down, I began interviewing to be a data analyst at a Fintech startup. I started that job in September 2020 and lasted about 9 months, but I was badly depressed at the time and an underperformer. In April 2021, the day I got the first vaccine dose, I decided to resign -- thereby saving the company the hassle of firing me, which I think bosses generally appreciate -- and take a long hike to clear my head.

Stage 6: (2021): The Appalachian Trail

I wrote about this here.

Stage 7 (2021-2024): freelancing, one final hurrah in tech, and freelancing again

After hiking the AT, I went to New Orleans for a family event, and while walking around the Bywater, decided I really liked it and I wanted to stay for a while. I got a lease on a bedroom in a former BnB and did a little freelance work for an insurance company and a wealth management firm. (I got both gigs through family connections.) 

Eventually I started to run out of money and felt like it was time to get a real job again. The week after Mardi Gras, I saw a job ad on Hacker News that that looked appealing. I joined that company in May 2022 and moved back to NYC shortly after. (I/we wrote about that company's mission for the forum a few times, and shared my initial cover letter in a blogpost.) 

Throughout this period, I worked on and off on a meta-analysis of interventions intended to reduce sexual violence (as a hobby) with a team based in Princeton. Here is a write-up.

In summer 2023, I sensed that my time at my employer was coming to an end, and I began looking for an offramp. (You might be sensing a pattern here.) In August, I went to EAGxNYC 2023 and met some cool people, including someone who works at ASAP, and we began a project looking into interventions that try to reduce consumption of meat and animal products. (Here is a first draft.). As part of that project, I emailed Maya Mathur a question, and it turned out that she sometimes used the paper I worked on in summer 2016 in her teaching and also in a textbook she's co-author on. We met in person in January 2024, began working together shortly thereafter, and as of now I am a research scientist at her lab.

What did I learn?

In case this wasn't clear, I have typically had no idea what I was doing or where I was going. But things have turned out ok nevertheless. Sometimes I feel like I just got lucky. I also observe that being person with a lot of privilege(s) who can rely on savings and social networks when things go badly helped me fail up. But perhaps I've nevertheless learned a few things worth repeating:

  1. if anything in this journey went well because of things I did, it was usually because something caught my attention in a hard, biting way and I became obsessed with it. These were not projects I undertook for instrumental reasons; I usually have no clue what doors are ahead of me or where I'm going. But doors open anyway. 
  2. I've sometimes heard that you should aim to work for people who are where you want to be. I'd say instead that I work best for people whom I admire but whose positions I do not necessarily covet. I have never craved the stress of being a high-powered person. Lieutenant, or even batman, is a better fit.
  3. Rejection, even a really big one, is not necessarily the final word. I went to grad school to be a researcher, and eventually I got there.
  4. Nick Bostrom remarks in Superintelligence that for all we know, a state of sullen anxiety is optimal for getting work done. And indeed when I get a bee in my bonnet, I'm generally not happy about it. 'Annoyed' is closer to the mark. But that's apparently the motivation I need for really detail-oriented data work. So I guess I'd ask of anyone who asked me about career matters: does anything really grind your gears? Is there a project you can make out of that? Do you have something you want to say? And not to worry so much about what will come of it. Start by doing work you're invested in, that you're proud of, and people may notice. 
  1. ^

    Andrej Karpathy observes: "Learning is not supposed to be fun. It doesn't have to be actively not fun either, but the primary feeling should be that of effort." But for most of my life I acted like a praise-seeking missile, which generally meant focusing on things that came easily and where learning didn't feel like work. 

  2. ^

    I also took the LSATs and was going to apply to law school, but sitting in a meeting one day, bored stiff, I was struck by the thought: "I don't want to be in meetings for the rest of my life. I shouldn't be a lawyer." So I withdrew my applications and I think this was a good decision

  3. ^

    The organization was restructuring amidst some unexplained budget irregularities, and I was among those who fell somewhere between "left" and "asked to leave." I still don't have total clarity into what happened behind the scenes, but I received a poor performance grade on an ad hoc evaluation and was given a performance improvement plan; when I asked my boss and my boss's boss if there was any viable path back from the PIP to a future at the organization, they strongly implied that there was not (though they never came out and said it, presumably for legal reasons). My boss was also on their way out. 

  4. ^

    The company went through a serious change in culture and focus as a result of a fundraising round in summer 2019, and the organization that emerged from that crucible worked on things that didn't speak to me.

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Thanks for writing this. I found it really moving and admire your perseverance throughout the process

Thanks for sharing this candid account of your journey, Seth! How fortunate for the HSF Lab that your path landed you with us. Even though the trip was roundabout, it gave you a breadth and depth of skills, and worldliness, that doesn't come rolled up inside a PhD diploma.

Start by doing work you're invested in, that you're proud of, and people may notice.

This is fantastic advice.

Executive summary: The author describes their winding career path from aspiring political science professor to research scientist, highlighting how unexpected opportunities and persistent interests ultimately led to a fulfilling role despite numerous setbacks.

Key points:

  1. The author's initial goal of becoming a political science professor was derailed by poor performance in graduate school.
  2. Multiple career transitions followed, including brief stints in tech, freelancing, and hiking the Appalachian Trail.
  3. Persistent interest in research and meta-analysis projects eventually led to opportunities in that field.
  4. Failures and rejections were not final, as the author ultimately achieved their goal of becoming a researcher, albeit in a different field than originally planned.
  5. The author emphasizes the importance of pursuing projects that genuinely interest you, even if the career path is unclear.
  6. Privilege and social networks played a role in allowing the author to recover from setbacks and continue exploring career options.

 

 

This comment was auto-generated by the EA Forum Team. Feel free to point out issues with this summary by replying to the comment, and contact us if you have feedback.

Thank your for sharing your honest and detailed career path! Full of bends, unexpected rocks, steep hikes... Only could you if possible elaborate on your transition to IT/tech? You mentioned that you applied for jobs as data analyst. Did you start learning coding, data analysis, DevOps...? IT is extremely wide and I also think that nobody should be scared of giving it a try because there's always a niche they will enjoy. How was your experience and what branch of IT have you focused on?

Hi Victoria, thanks for asking!

The stats classes I took in grad school typically had problem sets in R, so I learned that. I got better at it in summer 2016 when I used it for the paper I worked on. The first real job I got in tech was doing technical support for academic researchers who were using a computational reproducibility platform, so knowing a bit of R and being able to pick up enough of the other languages to get by -- mostly some shell scripting and package installation commands in Python/Julia/etc. -- was helpful. Mostly I just learned the bits and pieces I needed to know and didn't really approach the question systematically.

The data analyst job I got was in an R shop. If I had been more motivated by the problem and a better fit at the company, I might still be doing that.

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