Q: Tell us about your back-
grounds and how you met.
A: Sheltzer: I majored in molecular biology at Princeton University
and then went to grad school in
biology at MIT. I’m studying how
changes in chromosome number
affect cell physiology and cancer
progression. Joan and I met on
OKCupid. We started chatting
about our mutual love of Richard
Feynman and hydrogen atoms. We
met up at a local cafe and haven’t
looked back since!
Smith: I have a Bachelor of Science
in physics from MIT, and I’m a
software engineer at Twitter. From
just a few dates, it was obvious
that we valued the same things
(work, science, feminism), had
complementary personality quirks,
and got along exceptionally well.
Q: How did you start working together?
A: Sheltzer: I was analyzing some microarray data, and
I reached the limit of what I knew how to do in terms
of data analysis. So I described the scientific question to
Joan, and in about 30 minutes, she had set up a Python script to answer it. Collaborating with Joan really
expands the range of questions that I’m able to address.
Q: Where did the data for your PNAS article come from?
A: Sheltzer: Joan and I started counting the grad students and postdocs from the websites of a few biology
labs. We soon found a striking pattern—elite male faculty
in the life sciences hire particularly few women—but
we also found that it would be difficult to get a large
enough sample size to make the results robust and
representative of the life sciences as a whole. I had recently sold my car, so we ended up spending the money
I had made to hire freelance data scrapers to collect
more lab information than we could on our own.
Q: Are there any other important
points buried in the article?
A: Sheltzer: It’s quite striking how
a small number of “elite” labs function as gateways to the professoriate. We found that about 10% of all
faculty members are members of
the National Academy of Sciences,
but about 60% of new faculty members did a postdoc with a member
of the National Academy. I think
that this says something about
the insular nature of academic
science. It probably limits the
scope of scientific questions that
new investigators study. They’re
mostly coming from established
labs working on established topics.
Q: What skills are needed to
do data-intensive research?
A: Smith: Since the data is ef-
fectively limitless, the primary bounds need to be imposed
by your scientific question. Figuring out which pieces of
data need to go next to each other in a table is a good
chunk of the hard part of data analysis. Beyond that, you
have to know at least the basics of programming, and
you have to have enough of a background in math that
you have the confidence to figure out some new statisti-
cal method or tool that you haven’t seen before. But in
the end, the only things you really need are a computer,
Google, time, and the confidence it takes to figure stuff out.
Q: How do you envision the future of your partnership?
A: Sheltzer: Professionally, we’re working on a paper
together using Joan’s data-analysis ability to parse through
gene-expression data from more than 20,000 cancer pa-
tients. Personally, I’m hoping that we get a cat soon. ■
Jim Austin is the editor of Science Careers—@SciCareer
Editor on Twitter. Do you have an interesting career story?
Send it to SciCareerEditor@aaas.org.
“Elite male faculty in the life
sciences hire particularly
A scientific partnership
In late June, the Proceedings of the National Academy of Sciences (PNAS) published an article showing that elite male scientists hire fewer women (as postdocs and graduate students) than oth- er male scientists or elite women do. Almost as striking as the article’s main result is the makeup of its authorship team. They are a couple: Jason Sheltzer, a graduate student studying cancer biol- ogy at the Massachusetts Institute of Technology (MIT) in Cambridge, and Joan C. Smith, a soft- ware engineer in Twitter’s Cambridge office. This interview has been edited for brevity and clarity.
By Jim Austin