Land Your First Data Science Job

Contents Outline

Land Your First Data Science Job

May 09, 2020 11 minutes read

10 Practical Tips on how to get employed as a Data Scientist as soon as you graduate.

Image by StartupStockPhotos from Pixabay

You have worked hard for several months to learn the ’ins and outs’ of statistics and programming, and you have remembered all basic machine learning algorithms by heart. You probably can explain how the neural networks work and what is backpropagation, and how the weights in the model get updated.

Your best friend forgot that you exist as you have not seen him for a while as you were too busy trying to improve your latest random forest model by another fraction of a decimal place (which you finally succeeded after several days of trial and error).

But the latter is not important…

If you wanted to, you could do all this in a few hours. So you finally feel confident that you are a Master of Data Science! Or at least consider yourself a decent early adept.

Well Done! But now what?

Now you need to convince other people that you are a Data Science indeed and this maybe the harder than learning the skills itself.

Therefore I am going to share some tips with you that have helped me with my Data Science job searches, so if you are interested hang on…

Image by Ribastank from Pixabay

1. Define what type of job you want.

There are thousands of data scientist jobs available, but they may not all fit what you are looking to do. You are the one that knows what are your strongest parts in data Science and what interests you. You may be interested in traditional machine learning or love deep learning and have done several projects focusing on neural networks.

Are you a person that is more hand-on and likes implementing the algorithms and feels good about creating production-ready code?
Or you would rather read the newest research and try to implement the newest neural network architectures?
Do you want to work for a large or medium-sized company?Or perhaps you would rather work for startups?
Is the company culture important to you?
Are you willing to relocate and if yes what distance? Or perhaps you are going to accept the remote position only.

They are all valid choices but I recommend that for the first Data Science position you find a role where you will not be an only Data Scientist in the company.

You needs to have those answer before you start applying for any jobs as it wll save you time and effort.

2. Apply to as many relevant jobs as possible.

Once you have figured out what type of job you want you to start applying for the positions. It is important that you stick to the rules you have outlined for your job and apply it to the only relevant position.

I am going to repeast this again:
This is extremely important to apply to relevant positions only.

Data Scientist roles vary so much in their duties and you may end up doing totally different tasks depending on the role definition. You want to be sure that you will be something that you have knowledge of and keeps you interested.

Additionally, it will be hard to get a job in something that you are not familiar with or not interested at all. Even if you somehow succeed in the interview process you will be forced to do it for the next couple of months or a year probably feeling miserable.

All this while your perfect job was still out there.

3. Create a spreadsheet to keep track of applications.

You do not need to prepare anything fancy. Google spreadsheets will be enough. You need to keep track of company name, position you have applied, link to the job add, application date, and if you get a reply or not, and if you did the description of what the reply was e.g rejected, phone screen interview invitation, etc.

This is enough to have you organized, you will know how many jobs you have applied to, in what time span and it basically will help you to keep sane withing the process.

Sometimes you may feel like you have applied to hundreds of jobs and while you look at the spreadsheet you will see that in fact, it was a lot but not as much as you have thought.

Image by Pexels from Pixabay

4. Look at smaller job boards and company websites.

So there are the obvious general job boards that we all know about such as Monster, Indeed, Craigslist and they are good places to start. Definitively send the application to jobs advertised there but the number of applications that companies receive through these sites will be high and it may be hard to stand out.

Try to find smaller job boards that are Data Science related, or the niche-specific in what area you would like to work for. Good examples of places to look for jobs as Data Scientists are

I am personally have been working remotely for several years and have been interviewing for jobs posted on the above pages.

Additionally, for remote workers the good places to look for Data Science jobs are:

5. Check company websites for Data Science jobs.

Additionally to job boards, I would encourage you to search your local company websites and see if they are looking for Data Scientists. It may be the company policy to advertise on the website first and later move on to broader job advertisements.

As you can imagine company website applications should have fewer applicants so your chances of getting the interview will be higher.

Similarly, you can not only check local companies but find other ways of identifying businesses that may look for Data Scientists.

Good ways would be to check companies that your friend Data Scientist graduates work for. There is a chance that they may be recruiting right now and you even get recommended by someone that is in the company now.

6. Attend meetups and Data Science events and be ACTIVE there.

Another important aspect of the job search is attending Data Science meetups and other Data Science related events in your local area. Those are attended by people that are interested or are working in the field. Trying to connect with them can get you a recommendation to the company they work for and eventually lead to a job.

It is important to be active in these type of events.

You will not benefit from listening one hour lecture about optimizing gradient boosting algorithm and then watching a guy answering questions from the public.

You will benefit only if you are the active participant of the event. That is asking questions to the person who is presenting, talking and interacting with other participants. You never know when the person next to you may become another job lead.

Image by uh_yeah_20101995 from Pixabay

7. Cleanup the GitHub account and upload good projects.

For any Data Scientist roles, it is almost a requirement to send a link to your GitHub profile with your CV and cover letter. Whereas more experienced data scientists can get away with almost empty GitHub profile it will be hard for a newcomer to the field. Finally, you are trying to prove that you can be a Data Scientist and this is a great opportunity.

Thefore clean up your github account and add excellent projects that you are proud of.

The latter also means removing all the garbage that is irrelevant and does not showcase your skills. Show your best projects on the top so those will be the ones that the recruiter will see when he opens your GitHub profile.

I recommend having some projects with nice visualizations. We are people and we are attracted to attractive visuals more than the code.

Additionally, some recruiters may not be very technical and may not understand the details of code so catching their eye with beautiful visuals usually does the trick.

Jupyter notebook autocompletion

8. Have a website with a portfolio.

This is not a requirement for any job application but could be a nice bonus while trying to convince someone that we are good at something. There are so many online editors that allow you to create websites so you can choose any of the free and popular ones or use GitHub Pages. There are plenty of tutorials online that teach you how to do it.

Make sure your website is attractive and describe there five of the projects that you are proud of. Again nice visuals are the bonus.

You want your website to be catchy and show that you are a proffessional in what you do.

Do not be technical in your description and treat the visitor more like a business or product expert. You need to convince them why the projects you have done are being valuable to the world. If they are interested in technical implementation they can look at your GitHub page, so make sure you link your project there.

And again, there are people who do not have their websites (including me) and they get hired. However, those people rely on their previous experience so they do not have to work that hard to prove that they can do the job.

As a newcomer to the field however you need to try to impress someone on all levels. The better the impression you make, the more chances you can get the interview. Therefore spending a few hours on the website creation can really be worth it.

Image by StartupStockPhotos from Pixabay

9. Learn three projects that you have worked on by heart and be able to story tell around it.

So this is the advice for interview preparation, this means you have impressed someone enough to give you a phone call or maybe even an in-person interview. You need to impress them now even further with your Data Science knowledge.

I suggest that you revise the three projects that you have worked on, revise the code, remember the setting of the project, and all little detail about it.

Where you working on your own or or in team?
What tools you have been using?
What algorithms did you use?
What were the results?
What metrics you have used to evaluate the results?
Did you have any challenges?
What would you do different if you were going to redo the project?

You need to be able to answer all this and similar questions about three of the projects. And why do I say three? One is not enough?

You want to have three projects prepared so you can pull up any of them when the interviewer asks you about the experience. There are likely to ask you several questions so you want to have a variety of the scenario prepared and not look like you have just worked on one big project only.

Even if the question is not exactly the one stated above, usually preparing the above answers and rereading the project's code will have you prepared for the mots of the interviewer questions.

Also, note that some questions from the interviewer are not asking about the projects itself but you should answer using project reference. E.g questions like ‘what classification algorithms do you know?’ would be much better answered using the project reference rather than providing a list of algorithms only.

You can practice by actually finding websites with Data Science interview questions and try to answer them using three project references that you have chosen. This should have you prepared for a lot of experience and technical questions that the interviewer may have.

10. Learn about the company and the person that will interview you.

Being prepared for the interview is crucial. You do not know every detail about the company but knowing the general overview of the business is always a good idea.

Visit their page and see what they offer?
Who are their potential clients?
What is their business strategy?
What is their Data Sciene team?
What are the company values?

This information can be usually found on LinkedIn, Glassdoor, or the company’s website. Knowing some details about the company is your advantage and will make a conversation with the interviewer more fluid.

It is also a good idea to learn a bit about the interviewer himself before the interview. Usually checking their LinkedIn should be enough. You can also see if they have any blogs or have any interesting online presence.

Having this knowledge will not make you pass the interview but should make the conversation more pleasant and probably help you to be less nervous. It is easier to talk to someones that you know a bit if background than the total stranger. At least this is what helps me!

If you feel ready to work as a data scientist, this is a good place to find a fully-remote job.


These were my ten tips that should help with landing your first Data Science job. Obviously, there are many more things that you could do for interviews and job search preparation but I believe that following these simple ten steps will already increase your chances of getting a job significantly.

I hope that you have found the information helpful and good luck with the search!
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