A normal search for an entry-level data science job often goes like this…
You hit Google, type entry level data science jobs. Indeed.com and Glassdoor.com are at the top. You follow the links and find a very impressive list of companies looking for junior data scientists.
So you click on each one and start reading the job descriptions, job requirements and salaries and everything is very amazing, except one thing. Want to know what it is?
They need at least one year of previous professional experience as a data scientist.
They said they needed a beginner? What part of a beginner has to do with having previous work experience? Are they nuts or something?
I know it sounds really frustrating.
You need experience to get an entry-level data science job, but without the job, you can’t get the experience. So how do you get experience if no one will give you a job to give you experience?
Well, in this article we are going to look at a simple 5 step process to use to get a junior data science job even if you don’t have experience. It is part of my guide on how to become a data scientist without a degree in 2021.
If you follow all the steps I’ll outline in this guide, you’ll realize where most junior data scientists get it wrong, fix it and stay ahead of the competition. Then you won’t grind your teeth again whenever a recruiter asks you for experience for a junior data science job.
Let’s get started.
I know I said that I am going to show you how to get a job in data science without experience.
I lied. You CAN’T.
Let me explain what I mean by this.
If you can’t demonstrate some level of experience working with data and creating models, then no one is going to trust you enough to pay you for an entry-level position.
Heck, even an internship position where you are literally expected to work for FREE and learn will expect you to demonstrate that you are really into this field, and not someone who is just up to waste their time.
So how do you get this hard to get experience?
Build a portfolio of data science projects that clearly demonstrate your skills in Python or R (or any other data science language), a database like SQL and data analytics skills.
Once you learn all the skills you need to become a data scientist, then the next thing you need to do is pick low to medium level complication data science problems and solve them. You solve by building machine learning models, feeding them with data and tweaking them to provide the results that you want.
In the process, you’ll learn a lot. The much that you could have learnt at a junior level data science job. That is the experience you are looking for.
I’d suggest you work on 2 – 3 amazing projects.
If you have no idea what to start with, why not just Google some data science project ideas for beginners to get a hint?
Once you’ve built a portfolio of projects, head over to step number two.
If you ask any coder whether they are on GitHub, they most definitely would reply in the affirmative.
Because this version control platform serves as an excellent way to share your code, demonstrate your expertise and attract recruiters looking for similar skills.
In fact, there are some recruiters who just go to GitHub and start their headhunt from there.
Well, how does GitHub fit into your strategy for landing an entry-level job in data science?
Take the projects that you built earlier in step one and upload the code to GitHub. They were a pet project, no one is using them in production anywhere. So making the code publicly available shouldn’t be a problem, right?
But don’t just Git push the code, then sit back and relax and wait for recruiters to beat a path to your door.
If you want your project to really communicate how awesome a coder you are, provide a proper description of each project.
Here are a few tips.
- Describe what the application is all about, what it does, how it does and what kind of input data it expects.
- Provide a clear and easy to follow user guide for someone who wants to try out this model on their own project, what they need to do.
- Provide links to where you deployed this application so that someone can go there and have a feel of what you are talking about.
Once you have built three amazing data science applications, uploaded the code on GitHub and spiced up your profile, we are ready to go onto LinkedIn.
Just like GitHub, any serious coder knows of LinkedIn, not unless you are a dinosaur reading this article in 2021.
LinkedIn is a networking platform that enables you to connect with other professionals in a similar profession as you.
You heard that right. I know most data science engineers think that once they have the skills nailed down, head hunters should queue, begging to interview them.
A few years ago, YES, you could expect that. Not anymore.
So how does LinkedIn fit into your strategy for landing your first job as a data science engineer?
Clean up your LinkedIn profile and spice it up to reflect your current skill set so that when a recruiter is searching for a data scientist they can easily find you.
It works in a similar way to GitHub. A recruiter would put certain keywords of what they are looking for in the search field, LinkedIn would then filter the results and return profiles that mentioned that particular keyword in their profile.
Here are three tips for dealing with LinkedIn.
- Complete your LinkedIn profile to reflect all the data science skills you’ve acquired.
- Provide links to your projects on GitHub or where you deployed.
- Send out connection requests to profiles with data science in their titles.
Your network is your net worth.
Now this works two ways in your favour. If you set up your profile right, recruiters will organically find you and contact you.
Again, by connecting with other professionals in this field, you are putting yourself on the radar of someone who might be hiring, or who knows someone who is hiring.
If there is a coworking space nearby, why not show up a couple of days a week and network with their community? From my experience, programmers and developers frequent the events and gatherings organized by these coworking spaces. So you for certain might find someone with whom to expand your network.
One last thing about LinkedIn.
You should remain active on the platform. People have a short memory, so if you want to stay on someone’s radar, you need to do something that will keep making your name surface.
And this you do by, for example, commenting on posts related to data science or publishing your own data science related articles on the LinkedIn pulse.
Just be respectful and don’t start a fight 🙂
If you’ve come this far and nobody has noticed yet, don’t lose faith or hope. Hope is the last thing you want to lose and then die.
Here is another super amazing thing you want to get into that will enable you to put your skills out there, while also learning a ton of things. Basically, you are killing 3 three birds with one stone.
You ask how?
Well, participating in a competition goes down like this…
You hit a data science competition listing a platform like Kaggle, find a competition description that describes your area of interest, or what you want to work on. Join the team and start working on providing a solution to this problem together.
If you don’t the way Kaggle home page looks like then here are other data science competition platforms to check out.
Here are three ways joining a competition helps.
- You’ll get to put the skills you’ve acquired to practise by working on a real problem with other data science engineers.
- You’ll get to learn about team collaboration and soft skills, which are very important when it comes to keeping a job after you get it.
- You’ll get to connect with other professionals, broaden your network and increase your net worth, by simply increasing your network.
And one other thing I don’t want to forget to mention is that these competitions have a price tag.
You win, you carry the MONEY home. (How about that for motivation?)
I don’t know about the recruiters from your area, but I would definitely pick someone who has successfully participated in a data science competition for an entry-level position.
What I am trying to stress here is to work your way up to your dream job in data science without looking like you are.
This guarantees that you have fun while at it.
Imagine if you just spammed 250 data science entry-level job ads and got rejected by all of them.
How would that feel?
Would you still believe these stats that claim data scientists are in super high demand?
That will certainly deal you a super high dose of frustration, depression and stress. Or maybe those three words mean the same thing and I just repeat myself.
However, if up to this point you haven’t landed something yet then checkout step 5.
If you haven’t found an entry-level data science job yet, then here is your blank cheque to spam recruiters and data scientists job ads until either they give you a job or you run out of websites to spam.
No, I’m kidding 🙂
But if you already went ahead and did that then you know it didn’t take you any far, right?
The reason I kept applying for jobs as the last steps is because you need to be ready for this. Your profile needs to be the kind that would stand out among other applications.
At this point, if you apply for jobs in data science you’ll receive a lot more positive responses, interviews and finally a real job.
You know why?
Most people don’t wait to come this far before they start applying for jobs.
So their profiles almost always look the same, with no differentiating factor. This makes the recruiters work very hard. They don’t know what you really can do. Put yourself in the shoes of the recruiter. Would you hire someone who only claims to have potential or someone who has already demonstrated that potential?
I’ll let you answer that at home.
That is how I’d go about applying for jobs. And at this point give yourself some freedom.
It’s a numbers game. But done right.
The job sites where I frequent when looking for ads are LinkedIn, Stackoverflow, Indeed.com and Glassdoor. In addition to that, the Jobble data science job listings has a ton of fresh listings that are a great fit for junior developers.
Read the job description carefully, then write a job application describing your experience and how you can use your experience to add value to the company.
It, therefore, means that you have to at least go to the company website to learn about their products and services.
At least mention something that shows you actually know something about them.
You’d be surprised how many people never do this.
They see a job ad for a data scientist and fire their resume. The same cover letter and resume over and over…
Mention the projects you’ve built, the problems you were solving and how you achieved them. How you could apply the same techniques at their company. Provide the links.
Don’t forget to mention the competitions you participated in.
Once you do this, you’ll start getting replies, interviews, coding tests and nicer rejections.
Just be prompt to reply when there is something positive and follow through with it. You’ll land an entry-level data scientist job, heck even an intermediate level job, sooner than you imagine.
Good luck 🙂
You see how trying to get an entry-level position in data science can actually lead to a frustrating wild goose chase with no end in sight?
Abraham Lincoln once said that if you give me 6 six hours to cut down a tree, I’ll spend the first 4 hours sharpening my axe. What did he mean? You’ve got to prepare. Have a strategy. Have a plan.
Don’t just throw crap on to the wall and see what sticks.
I hope this guide has provided a clear strategy to follow in order to get your first job as a data scientist.
If you have not built your portfolio yet, which is the very first and important step, why not use these data science resources to pick up the skills you need to build an amazing portfolio.
Are you a beginner in data science or a senior data scientist with many years of experience behind you?
What are some of the strategies for getting an entry-level data science job that I did not mention in this guide?
Please share your thoughts in the comments below.
Lerma is our expert in online education with over a decade of experience. Specializing in e-learning and e-courses. She has reviewed several online training courses and enjoys reviewing e-learning platforms for individuals and organizations.