How to Build Your Personal Brand as a Data Scientist

Personal branding is a thing now. It always has been, but I believe it’s been
getting more and more attention recently. More people are aware of its
importance, including the employers. Giving you a big paycheck, assuming you’re
good, is obvious. Providing opportunities to flourish and build your personal
brand is something an increasing number of companies are trying to seduce you
with.

While working in the algorithms group at Taboola, I was encouraged by the
company to share my knowledge with the data science community. It has motivated
me to embark on a journey to build my personal brand as a data scientist, and I
want to share how I did it with you.

There are many paths you can choose in your journey to build your personal
brand. The problem is that your time is limited. There’s a lot of uncertainty
about each path and its outcomes, so it’s best to make educated decisions.

There are several criteria by which I judge each path:

• How likely is it that I’ll get to the finish line?
  This is the most
  important criterion when you just start building your brand. If you fail on the
  first path you tried, it’s gonna be a motivation killer, and you’ll quit.

• How much time will it take?
  Together with the previous criterion, it
  enables you to better manage your time. Maybe it’s ok to choose a risky path,
  given it’s only gonna take you a small amount of time.

• Will I learn something new?
  Data scientists have to position themselves as
  experts in their domain. In order to be an expert, you must keep learning all
  the time.

• What will the final product be?
  At the beginning of your journey, the
  product can be mild — maybe a nice blog post for beginners. As you gain more
  experience, you should put more emphasis on doing more meaningful things. Maybe
  a blog post about an advanced aspect of ML, or even trying to win a Kaggle
  competition.

• Will I enjoy walking along the path?
  At the end of the day, we do what we
  do for fun, right? 🙂

So which paths exist?

1. Blogging

For me, it’s the path of choice. The advantages of having an active blog are
many. But how do you choose what to
write about?

The most obvious option is to write about something you worked on as part of
your job. Most people do interesting things. The sad thing is, they’re not aware
of it. They think their daily tasks won’t be interesting to others. I claim it
to be not true.

Another option is to write a post about a paper you’ve just read. I believe
providing value is important — merely summarizing a paper can be done by almost
anyone. You must make your post unique. I love to write code that accompanies
the post, for instance. In fact, most of my posts are Jupyter
notebooks.
There is plenty of ML material on the web, but code samples that demonstrate how
things actually work are hard to come by…

A third option, which is for the more experienced data scientists, is to write
an opinion post. Why is it for the experienced? Inexperienced data scientists
will be able to deliver their tasks. Experienced data scientists will be able to
state their opinion about what is the best way to do so. Which framework is
better for the task? Is this paper any good? When should you use this
visualization and not the other?…

Another somewhat harder option would be to write a post as if you were trying to
teach a subject in class. You’ll want to write the post in a concise, short, to
the point manner. If you can do it well, there’s a lot of value in it. In my
Intro to Statistical Tests
post
I took a well-known concept — statistical tests, and tried to explain it to
newcomers. I also added code, so it’d be even easier to comprehend. Writing this
kind of post will allow you to better understand the subject yourself!

2. Working on a side project

Try to think of a cool project you can build in a couple of weeks/months,
without putting too much time into it. Once you’re done, you can show the
project to the world!

Recognizing in advance that one of the goals is to share the project, you can
choose which project to do more wisely. For example, solving MNIST won’t be
interesting. You must choose something original. Here’s an example of a post I
wrote about Word
Morphing which I
believe was interesting because of its originality.

One of the cool things about side projects is that besides the project itself,
you can write a blog post about it. If you have an idea of a project which is
the material for a blog post — it’s a great sign the project will be good.

3. Kaggle competitions

Try to compete using a model you’ve never tried before. You might not win, but
you will gain knowledge and a bit of experience with that new model, which you
could share using a blog post! The winners of Kaggle competitions write blog
posts all the time. There’s no reason why people who don’t win won’t write blog
posts as well.

4. Meetups and conferences

Strive to give talks at interesting Meetups/conferences. The first time I gave
a talk at a Meetup was
because Taboola was asked to give one, and I took the bullet. Only after the
talk did I realize what a cool and fun experience it could be.

When I heard about Reversim summit, the
first thing I did was to submit a talk proposal. Submitting is easy, and no harm
is done if your proposal gets rejected. I was lucky enough to get mine
accepted.
Don’t get me wrong, it required preparation. But there’s a lot of value in it.

Even if you just attend an interesting conference without giving a talk, you can
write a blog post about it. It builds your brand as someone who goes to
conferences and shares knowledge with the community. Here’s an example of a
post I wrote after attending an
interesting conference.

5. Contribution to Open Source projects

If you become a significant contributor your brand is gonna shine. If we’re only
talking about small contributions I’m afraid it might not do. But that’s the
nice thing though — you can put in a small effort, see if you like it, and if
you do — invest more time in it with the goal of becoming a significant
contributor.

6. Publishing a paper

If you have published a paper in a known proceeding, it’ll positively affect
your brand. But if you’re not in the academy, or if you don’t work in a company
in which it’s part of your job to publish papers, then the risk is huge. You’ll
need to put a lot of effort and time, and there’s a high probability your paper
won’t be accepted.
I was lucky enough to publish a paper with my coworkers
at
Taboola,
but I’m afraid I won’t go through this path in my spare time — because of the
risk.

Building your personal brand is a journey. There are no right or wrong paths to
take. It’s all about your style, what you like more, and how much time you have
to put into it. I believe that all data scientists should put some effort into
spreading their knowledge. It can be fun, and it’s awesome for your brand, as
well as the brand of the company you work at.

Do you have some other path you’ve taken that worked better for you? Let the
world know by dropping a comment!