Before I became a data scientist, I spent a lot of time Googling “How to get a job as a data scientist” and browsing r/DataScience. Maybe you’re trying to become a data scientist, too, and you’ve somehow landed on my blog. Welcome!
I’m writing this article for 2 reasons:
- I want to help you! I was lucky to receive a lot of help from generous members of the data science community when I was first starting out. This article is one way I can give back to people who are in the position I was in. We all have to start somewhere!
- I find I often give the same advice over and over again to people who reach out to me to chat, so I’d like to put my thoughts down in one place. Hopefully this article can be helpful to you in some way.
Feel free to reach out to me if you have any further questions and if I can help in any way!
Please note that this article is geared to those who are looking for their first job out of school, which was my situation, and in this post I’m simply speaking from my experience.
First thing’s first… what is a data scientist?
The term “data scientist” is extremely broad and poorly defined. I’m going to loosely define “data scientist” as someone who works with data, does analytics, experimentation, and builds predictive models to solve some business problem, and understands the mathematics required to do so. They use machine learning, have strong coding skills, and are comfortable reading academic machine learning literature. They may be stronger with math than they are with coding, or vice versa, but they are competent and comfortable with both. It’s a highly technical role.
Background, proof of work, and networking; aka the 3 C’s
The way I see it, there are 3 main things you absolutely need in order to become a data scientist. You need the right background, you need to prove your work, and you need to network.
Or, as my lovely father has reminded me (after having posted this, oops!), it’s the 3 C’s: Credentials, Credibility, and Contacts.
It’s important to have the right background in order to become a data scientist. A quantitative background in mathematics (I’m biased!), computer science, physics or engineering is great. So is any discipline where you use data, think about experiments, and use math in some capacity. Those disciplines include but are not limited to: economics, psychology, biology, geography, chemistry, political science, etc. A math or comp sci background is sufficient, but it isn’t necessary. Just remember, a data science role is a technical role.
If you come from a less “orthodox” background, don’t worry. Unique perspectives are great, and should be welcome, if the company you’re applying to is worth working for! Just be prepared to put in the work to develop the skills required to become technical. And when you start out, if you’re lacking in one area and are stronger in another, that’s completely ok. The most important thing is to be prepared to learn, regardless of your technical background.
Credibility (Proof of work)
And, regardless of your background, you need to prove your work.
“Proof of work” is anything that publicly displays your data science skills. It means you have a portfolio of personal projects, or you’ve written a thesis (and that thesis is available to anyone), or you’ve done some projects at school, etc. Proof of work is important because 1) it allows people to verify that you actually know what you’re talking about, and 2) it allows people to understand your thought process, workflow, and decision making.
The “public” part of this proof of work concept is important. It can be a bit daunting at first to put yourself out there, but it pays off, and can be a lot of fun. On my end, I initially created my blog because I wanted to prevent myself from forgetting all my projects. I thought writing about them publicly would keep me accountable (it did, and it does). It turned out well, and my blog turned out to be one of the best things I did for my job hunt when I was looking for my first job.
At the very least you should have a GitHub account, with repositories that show off your data science projects. Even better would be a blog (again, I’m biased). Showing and proving your work in this way is an opportunity to show people that you’re more than just your credentials. You can show them who you really are!
Speaking of showing people who you really are, a great way to do this is through networking. I like to think of networking as “meeting new friends who you can learn from.”
When you’re looking for a data science job, it’s extremely important to network. Through talking to people who do the job you want to do, you can:
- decide if you actually want to do the job,
- figure out what specific skills you need,
- learn about the culture of their company, and if you’d like to work there,
- be informed about the interview process, and get tips,
- learn about salary expectations,
- get a referral to their company immediately,
- get a referral to their company in the future, or
- make a new friend (the best point in my opinion!).
A networking chat may not check off every point I’ve just listed, but you certainly will gain something out of it.
My advice (or at least, what I did) is to make a list of data scientists who live in your target city, and learn about their backgrounds, and their current companies. Decide what interests you about them, and send them a message via LinkedIn or email saying something like:
Hi X, I recently graduated with a degree in Y and am looking to work as a data scientist. I’m really interested in your work in Z, would you be open to chatting more about it over coffee/a phone call? I’d appreciate it, thanks!
The key here is to make sure that you’re genuinely interested in learning from them. You can learn so much by just listening to peoples’ experiences, and generally people are very open to helping, especially if you treat them to coffee.
Some points to keep in mind when you do meet the person:
- Prepare your elevator pitch, and also make sure you tell the person why you wanted to meet with them.
- Your reason to meet with them should be more than “I want to meet with you because you’re a data scientist and I think by talking to you you’ll somehow get me a job.” Are you interested in meeting with them because of their background? Their industry? Do they have a social media presence that fascinates you? Do they have a blog? Make sure you let them know.
- Be genuinely interested in what the person is saying. This is a chance for you to learn from them!
- Don’t just ask for a referral or a job. If the person likes you (which they should if you’re genuine), then they’ll try to refer you to a job if an appropriate one is available.
- Go into it with a mindset that you can learn from this person, and that’s extremely valuable. Everyone has a unique experience that you can learn from.
- It always helps to offer to pay for coffee, and some people will expect that.
- Don’t think of networking as a transactional exchange. Remember, everyone you meet has something to offer you, whether that’s knowledge, a referral, or friendship.
- If you don’t get a referral now, that doesn’t mean you won’t in the future.
- These data scientists you meet now will become your peers in the future. The data science community is small.
- Send a message after you meet thanking them for their time, and again, be genuine.
- If you want, you can ask if they know anyone else you could meet with.
- Just be nice!
Trust the process! It works!
And there you have it. My three tips for becoming a data scientist: Have the right background, prove your work, and network. Please feel free to call me out on anything I may have said that’s not quite right. All of this is just speaking from my own experience, and what worked for me. Hopefully you’ve learned something from it!
Also published on Medium.