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Tyler Otto is a data scientist at Reddit with a PhD in experimental
condensed matter physics completed at UC Davis in 2013.
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Tyler Otto completed his PhD in Physics at UC Davis in 2013 with experimental condensed matter physicist Professor Dong Yu. Formerly the head of data science at Hipmunk, Tyler is now a data scientist at Reddit. He lives in Davis, has spoken with our students in our alumni seminar series, and is generally interested in contributing to his alma mater.
Recently Tyler, Professor Ethan Anderes (of UC Davis statistics), and I met with six of our current graduate students in physics to hear about their research projects as we looked for ways we could potentially help with the data analysis challenges they face. After the meeting, Tyler realized one way he would be happy to help out would be to offer his thoughts about how to transition from doing a PhD in physics to working in the field of Data Science. The rest of this post is the entirety of the resulting email from Tyler to the students.
I want to start with some of the concerns that people have
about freshly minted PhDs.
1.
Overly academic:
As a PhD student, you worked on projects that took years to complete, and you
were driven by trying to understand the most challenging phenomena. Contrast
this with what most businesses need, quick insights that can be acted on
immediately. Most companies are not at the level of sophistication that your
respective field is (your field of science has likely been around centuries,
far longer than most businesses). In physics terms, most businesses are still
learning their kinematics equations, but many new candidates talk about Quantum
Mechanics.
2.
Communication
skills: You have been surrounded by people that speak your same language
(science, I mean), and are generally deeply knowledgeable of the types of
things you are working on. Similarly, when giving a talk, you are used to
presenting to people that want to understand every detail of what you have done
and why you have done it. As a result, academic researchers often develop a
communication style that is very different than a typical person in industry. As
a Data Scientist you will work closely with marketing teams, engineers,
business development, VPs, etc. These people are all very smart, but require
that you learn to adapt your communication style.
3.
Too specialized:
This is the easiest problem to address. You spent the last 6ish years thinking
about a particular set of problems, and likely addressing them with a handful
of tools. For many of us, these tools did not include lots of programming,
machine learning, statistics, etc. How the hell are those hundreds of hours in
the clean room, or in front of an AFM going to make you a qualified data
scientist!?!
Let’s address ways that you can convince potential employers
that their concerns do not apply to you
1.
Focus on impact:
Many inexperienced candidates tend to talk a lot about tools, and interesting
problems. Instead, try to identify potential problems that the company may
face, and explain what you can do to make an immediate impact on this problem
(this also shows that you did your research and are somewhat knowledgeable of
the company). I also suggest showing that you can scale up your complexity.
“Here are some simple ways I would initially approach this problem for quick
results. If we see the impact we are hoping for, investing time in a more
complicated solution such as (insert buzzword)”
2.
Have an elevator
pitch: You should be able to explain, in 30 seconds or less, what your PhD
research was, why it was important, and what contribution you made to the
field. This sounds hard...it’s not! Some people may ask you to go into some
more details, this is not an excuse to to show off how smart you are. Spend a
few minutes (or less) touching on some of the broad strokes of your research.
If you did something cool, like work at CERN, feel free to mention that (not in
a bragging sort of way). This is not a conversation with a fellow researcher,
this is a cocktail conversation. I believe this is the hardest problem to
address on this list. We have developed our communication style over years, it
is going to take a lot of work to undo some of these habits. On top of being
the hardest thing to change, it is the biggest reasons that I reject
candidates. START PRACTICING NOW!!!
3.
Identify your
core competencies: No one is hiring a recent PhD in physics because they
are a machine learning expert (unless you are). They are hiring you because you
are smart, have excellent critical thinking skills, have worked in
collaborative environments, learned to ask hard questions (and answer them),
and more. Convince yourself of this and be able to articulate it to a potential
employer.
4.
Learn the tools
(but don’t be a cliché): This is what most people focus on the most, but it
is the most easily solved of the problems that new candidates face.
Inexperienced candidates tend to throw around a lot of buzz words (neural
network, machine learning, collaborative filtering, etc). This is the
equivalent of interviewing a contractor based on their discussion of hammers,
drills, saws, and levels. This should not be the focus of your discussion, but
it is important to indicate that you have some experience with these tools. An
exploding number of online resources has made it trivial to gain some basic
facility with the tools you will be expected to use. There are tons of other
sources sharing which skills you need as a data scientist. Do a bit of research
and focus on the ones that come up repeatedly. Focus on the basics before
getting more sophisticated. You will likely do more basic statistics than you
will machine learning. You will certainly do more basic coding than anything
else.
Finally, I would like to share my thoughts on how you should
proceed from here and what you should expect:
1.
Start figuring out how to apply data science tools in
your research. It will make for a better conversation during your application,
and will likely improve your research.
2.
Start applying for jobs right away. It will give you an
idea of what the application process looks like, and it will help you tailor
what you focus on. It will also help you work on your communication skills. It
will also help you optimize each of the hiring process.
3.
Work with your career center. They will help you with
your interview skills, resume, etc...this is an excellent resource!
4.
Expect it to take 6 months or more to land your first
job. Don’t get discouraged.
5.
Go to different meetups. They are boring, but they are
a great way to meet people in the field you are interested in, and a good way
to continue to develop your language.