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"Leaving the academic canyon"
http://johnstantongeddes.org/personal/2014/10/16/leaving-academ
Leaving the academic canyon
I’m leaving my career in academia as an evolutionary biologist to take a
position as a data scientist. Yes, the hype is true: businesses do want
people with analytical and computational skills. I’m excited about this
move because it allows me to continue applying my analytical skills even
bigger data, and learn new skills along the way (hello Hadoop!). Equally
importantly, it allows me to spend more time with my family in a place we
love.
Many people have written about leaving academia [1], so here’s my
contribution. Unlike others, my story is mostly happy, maybe cautionary.
Looking back on how I got to where I am, I feel the best analogy is going
for a hike in a box canyon. At the start, the canyon is wide, beautiful and
seemingly endless. About half-way down it starts to get narrower, but you
don’t worry because it’s still beautiful and you’re enjoying yourself.
But then, you get to the end, and the only way out is a steep climb to the
top.
correlation plot Box canyon near Sedona, AZ. Photo: John Stanton-Geddes
I got to the end of the canyon, and I could see the path out. I even think I
could have climbed there. But I’d had a good long hike, and surprisingly,
I found a side canyon. Climbing out was no longer the only, or even the best
, way to continue.
Back to reality, when I started my PhD in 2006, I had no skills to speak off
. I liked to teach. I liked biology. Getting a PhD sounded fun (and it was!)
and what other choices did I have with my liberal arts degree? So off I
went to get a PhD at the University of Minnesota. They were a tremendous 5
years. I learned enough to become a quasi-expert in my sub-sub-field, got to
do some great field work and lab work, taught undergrads, made great
friends, and got married. My PhD advisors were the best you could hope for
and only have (and still do) provide me with encouragement. My postdoc
mentors have also been great and supportive. I like to think that I had a
promising academic career. Hell, I’m up to 82 citations 3 years after
defending my dissertation (Google Scholar Oct 8, 2014) and in the process of
submitting a great paper to PNAS (where it probably won’t be accepted, but
it’s still a good paper!).
The catch is I now have skills of value. I wish I could remember what blog
or twitter post I saw this on, but it turns out that many of the
characteristics that make a successful researcher are the same
characteristics that make someone valuable to industry. I picked up a minor
in statistics and am a reasonably confident statistician. I spent a lot of
time working in R, and actually found that I enjoy programming. Which is
ironic given my main memory of ‘Intro to Comp Sci’ in college is that it
was the first (and only) class I skipped on a regular basis. I started to
learn to program in other languages, how to use linux, how to work on a
server, and other skills that are generally associated with the term data
scientist.
Thinking towards the future, here’s what pursuing a career in academy would
likely require:
Apply to 20 (or 40 or 60!) academic positions across the country.
If lucky, get asked to do an on-campus interview at at least one (maybe
two!) institutions.
Light candles and pray that the stars align so I get offered the
position, with (1) decent salary (non-negotiable as set by university or
union policy), (2) reasonable start-up so I can do research, and (3)
institutional support to succeed in teaching. For what it’s worth, the last
would have been the most important and probably least likely of my
requirements.
Teach 1-3 classes per semester consisting of a mixture of motivated and
un-motivated students paying the price of a new Tesla each year.
Spend hours writing brilliant grant proposals with about a 10% funding
rate.
Work my ass off so I get tenure or can “trade-up” to a better
institution or place closer to where I want to live.
Context: my postdoc funding runs out at the end of 2015, so I kinda sorta
need to get a job this academic hiring cycle. I have two kids so I need a
job. We live close to my wife’s family so the incentive to move is low.
To paraphrase something I read somewhere I can’t remember: “If I treated
my wife the way science treats me, she’d have left me long ago”.
In contrast, the data scientist position took three interviews and I was
offered the job about 6 weeks after hearing about it. Salary was better than
my median expectation as a starting professor, I asked (and got) more
vacation time. Of course, long-term this position brings up new challenges
such as will I succeed in the business setting, will my company value data
scientists, and what are my long-term goals. None of these challenges are
insurmountable or greater than the academic ones listed above. They’re just
different.
So, it turns out that the continental shift from academia to industry was
actually quick and easy.
Another related issue is that the people I respect and look to as examples
changed. As a student, it was my professors, and for the most part, I still
have tremendous respect for them. But the more time I spend in the analysis
world, I’ve found role models such as Hadley Wickham, developer many great
R packages who left his academic job to work at RStudio, Yihui Xi, also now
at RStudio, and Hilary Parker, data scientist at Etsy, that are doing
exciting work outside of academia. They set a great model for success, and
in a way that directly contributes to their communities (i.e. tax dollars)
to pay for universities and NSF grants.
I don’t think there’s a clear lesson here. I’m just another data point in
the figure that less than 10% of PhDs become tenure-track faculty. I don’t
regret any of my decisions. I’d never heard of R before I started my PhD
and certainly couldn’t tell you what a PCA was. I learned those skills
during my PhD, and had a great time doing so. It may have taken 3 times
longer than if I’d just gotten a masters, but it also didn’t cost my
anything other than my time. I got to meet many great people, think about
important questions, and contribute to valuable research. In the end, it
turns out that it is hard to have it all, for men as well as women.
So long, and thanks for all the data.
[1] I’d fall into the ‘Explainer’ category, which is consistent with my
philosophy to make my scientific work as open as possible…including leaving. |
