I know, I know. The word "branding" makes me gag a little too. But finding your data niche is so important because data science is a broad genre which simultaneously serves as both a category of work and a job title. So try not to go blind from rolling your eyes just yet; I'm going to put this in terms a Data Scientist can understand.
Take the below equation:
In order to find y, we need the optimal combination of X and ß . Apply this to finding a model for your brand: let y be equal to "a career path in the data science I actually want". Can you picture what the terms describing y should be? To me, X is equal to skills, ß is equal to interests and ε is equal to the unknown (more on that later). I call this method scientific soul searching.
Let's get searching
We'll walk through developing these features together. Think hard: the more honest you are with yourself about your skills, interests, and intentions², the more skilled and memorable you will be.
X - knowing your skills
Data science is a buffet. There are libraries and languages aplenty to keep your eyes glued to a terminal for years. Search job listings and see a broad distribution of skills: Python, R, SQL, viz, ETL, NLP, statistical analysis, deep learning, Hadoop, etc.
It would be tempting to try learning all of these to appear competitive, but this is the absolute wrong strategy. Each is associated with an entirely different skill set.
Instead, consider: what are your strongest skills? Forget about Python, R, SQL and any of the other basic prerequisites for now³. Think of an interesting analysis you did, or the time you took a novel approach to dealing with missing features. What is at the core of each instance? Dig deep - use your soft skills to highlight your hard skills. Set yourself apart.
𝛽 - knowing your interests and what's important to you
Take a moment to write down a handful of your interests, both professional and personal. Study what they have in common. Now go down the list, explain why you are interested in each.
Be true to yourself here. This step is key. I say this because "people don't care what you do, they care why you do it."¹ It's easy to get swept up in what's hot and pretend to be into something simply because there's an opportunity this week. Your honest enthusiasm about a subject and how data science can impact that subject will be apparent to the community at large.
You've got the X and 𝛽, now prove it with projects
This seems obvious, but I often receive messages from people who don't realize this to be true. You've got the brand, now you have to get to work! Here's my story to demonstrate this:
I had a handful of ideas for my Metis capstone project. I considered the formula I've presented to you: I wanted to highlight my NLP skills and a list of ideas, but none of them felt quite right. I did know, though, that I was drawn to the Supreme Court, a topic I've been personally fascinated with for almost 20 years.
I pushed forward with an idea to topic model every Supreme Court case in history, excited to talk about it to anyone that would listen. This enthusiasm was memorable: my project was passed around social media hundreds of times and this buzz resulted in a number of recruiter calls from tech firms like Google. The project has also resulted in plenty of speaking opportunities.
I'm writing this post now as a pioneer for NLP in legal aid - building complex multi-layer models that connect clients with the appropriate pro bono lawyer. This has been the most rewarding career experience I've had so far, and it came to me because the founder of the startup recognized my enthusiasm through my blog on the project.
Communication is how to tie the whole thing up into a nice brand
You've done the project, now tell us about it. Present it in a compelling way. Blog about it. Consider what would make you want to keep reading if this wasn't your project. Choose something you want to talk about because the more you write and talk about the subject, the more synonymous your name will be with it.
But wait, the dreaded Ɛ
It's easy to enter the market wide-eyed and ready to impress. You've sorted out your angles and are confident in your unique voice. But you see rejection after rejection for the coveted title of Data Scientist.
Just as regressions without controlling for the unknown, you've ignored lurking variables. Take a step back; assess what roles are within your reach right now. Your ε is a good opportunity to consider where you want to get, but aren't quite there yet. Take the role that fits in your career (pro)gression while continuing to build your brand.
Parting words: always ask why
If you've made it this far, you should understand branding as more about knowing who you are and what work moves you than it is about choosing the right color scheme for the fall campaign.
When you take it seriously, developing your data science brand is very fun. You get to meet others like you, so always ask yourself why you are interested. Doors will open to a much more exciting career path if you stay true to you.
- Simon Sinek's excellent talk on how great leaders inspire action.
- I didn't want to get too into intentions in this post, but don't be One of Those People who enters the industry solely for the money. If you are currently considering data science as a career, write down your reasons for pursuing data science and what you've liked and not liked about previous jobs. Be realistic about your current skills and abilities. Once you have this information, line up informational interviews to confirm with yourself. Hint: I have been known to occasionally accept informational interview requests.
- Are you an expert at any of the prerequisites? Can you describe a situation that proves that? Then absolutely highlight that skill! For example: you've been writing data processing modules in Python for 3 years and have contributed to sci-kit learn - your Python skill is your brand. The majority of Python programmers can't say that about themselves!