This article is a part of our in-depth Data Science Career Guide. To read the other articles, please refer to the table of contents or the links that follow this post.
We’ve covered resumes and project portfolios, the two most important aspects of any data science job application. But there are some other application materials you might be asked for, and other things to consider in the context of applying for data science jobs.
After all the time you spent building a cool-looking resume, it can be annoying to have to fill out a different online form for every company you’re applying to. Unfortunately, this is often a necessary evil. Even when you’ve made a connection in person, you’ll sometimes be asked to fill out the application online to get you into the official system.
Since there’s no way to avoid them during the job application process, there are some things you consider when filling in online application forms:
Avoid Applying via the Big Job Sites If Possible
If you’ve come across a posting on a big jobs site like LinkedIn, Indeed, AngelList, etc. and are being asked to fill out an application there, it’s worth taking some time to see if the job is posted elsewhere first. Often, you’ll find that on the company’s website there’s a more direct way to apply for the job, one that will allow you to submit a PDF resume directly rather than having to type it all into an online form.
Even if you encounter another online form on the company’s website, filling out the application that’s directly referenced on the company’s site is almost certainly a better idea than using “Easy Apply” on LinkedIn or any of the “easy” application features on big job sites. These features do make it easy for you to submit the same application materials quickly to dozens of different jobs, but getting a response from an application you haven’t tailored specifically for the job in question is quite unlikely.
Additionally, some hiring managers and recruiters spend less time on applications that come in via big sites, because for skilled technical positions these sites often produce a lower-quality average applicant than other hiring channels. So even if you find a job on LinkedIn, it’s probably best to apply via the company’s site if there’s a job listing there with a different application form or process.
Tailor Your Answers to the Job, and Include Keywords
On typical application forms, you’ll have to fill in most or all of the information that’s included on your resume. Instead of treating this like a tiresome copy-paste task, use it as an excuse to revisit your resume and tailoring each section to the specific job you’re applying for as you enter it (while remaining honest about your qualifications and avoiding typos, of course).
Particularly for entry-level positions that will attract a horde of applicants, there’s a good chance your form will be reviewed by a machine before a human ever sees it. That’s part of the reason so many companies require this kind of form-entry job application: it’s much easier for machines to scan these responses than to scan hundreds of different PDFs in various sizes and formats.
Your resume and application form responses should still be written with human readers in mind, but consider the robots, too. Identify important keywords from the job description and be sure those are included as you fill in the form. If employers are looking for a specific technical skill, they may simply be auto-rejecting out any applications that don’t mention that keyword.
Even human recruiters do this, in fact. “In that first pass screen,” SharpestMinds co-founder Edouard Harris says, “the human beings who are looking at your CV are pretty close to just doing dumb keyword matching. You want to have as much overlap of the keywords as you can.”
Including these keywords shouldn’t be a stretch, though. What you’re including on any application form should be a genuine representation of your skills. If you’re having a hard time getting keywords from the job description into your application materials, that may be a sign you’re not a good fit for the job and your time could be better spent on other applications.
Sharing and Preparing Social Media Accounts
Many job application forms will ask you to share social media links if you have them. Generally, whether you share is up to you, but there are two networks you’ll be expected to share for many data science positions, and you may need to do some work to get them ready.
For most data science and data analyst jobs, this one is mandatory. It’s where you’ll be showcasing your projects, probably, and employers will want to take a look at it regardless to ensure that you’re actively involved in doing data science work. There are a few things you’ll want to double-check before sharing your GitHub:
- Any repositories you don’t want employers to see should be set to private.
- You should have a profile photo or image that looks reasonably professional.
- You should have a username that sounds reasonably professional.
- Your contribution activity should show that you’re actively working on data-science-related stuff.
You don’t absolutely have to share a LinkedIn account, but since potential employers may look you up on LinkedIn even if you don’t share your link in the application, it’s best to take some time to be sure your LinkedIn looks right (or set up an account, if you don’t have one).
Specifically, double-check that:
- Your skills, employment history, education history, and other materials match what it says on your resume
- You have a reasonably professional looking photo. The photo doesn’t have to be professional quality — a smartphone camera is fine — but you have to look professional in it. LinkedIn has a quick video guide with some photo advice if you need it.
- Your profile headline and summary are related to your data science skills and career plan. If you’re switching careers, you don’t want any confusion for employers about what it is you do now (and that’s data analytics, data science, or data engineering).
- If you don’t have professional data science experience, your projects are linked in your “Projects” section (you can add a Projects section using the “Add profile section” button on your profile page).
- Your course certificates (like Dataquest certificates) and any data-science-related skill endorsements or recommendations you can get are on the page. If you don’t have a lot of data science work experience because you’re just graduating or switching careers, you want to be sure the message your profile communicates is clear: “I’m a data scientist.” Skills, endorsements, recommendations, and certificates can all help send that message.
Other Social Media Sites
Typically, whether you provide your employer with any of your other social accounts is up to you. But be aware that even if you don’t provide links, it can be pretty easy for a potential employer to find your online presence. Be aware of your accounts’ privacy settings and plan accordingly. You may decide it’s best to set personal accounts to private for the duration of your job search, or you may decide to leave everything open (perhaps doing a little bit of scrubbing before sending in your applications, just in case).
Either decision is valid, but you should make your decision knowing that potential employers can and probably will look at anything and everything you make public.
One of the things they might be looking for is a genuine interest in data science. It’s a good idea to engage with other data scientists and share work via social media all the time, but it may be particularly important while you’re applying for jobs. A potential employer who’s browsing your Twitter, for example, will probably be pleased if they see that you’re an active participant in data science communities there.
If a potential employer demands access to a private social media account of yours, you’ll have to decide whether or not you’re willing to provide it. Such a demand would be illegal in some US states, but even if the law protects you, pointing this out to a potential employer is unlikely to get you hired.
In many cases, it may be best to refuse the request and move on with your job search — an employer who does not respect your privacy during the hiring process is unlikely to respect it after you’re hired. But there are circumstances under which such a request might be reasonable. If you’re applying to a job that requires a high government security clearance, for example, you may have to be willing to submit to more scrutiny than normal.
Should You Include a Cover Letter?
Many online application forms will ask whether you want to include a cover letter with your application. You should consider this optional unless a cover letter is specifically requested in the job posting. All of the data science recruiters that we spoke to while creating this career guide said that cover letters are unusual. Most data science job applicants don’t include them.
As for whether you should include one, opinions were split. “Cover letters which are short, yes, that does make a difference,” says Ganes Kesari, co-founder and head of analytics at Gramener. He finds that they can offer some information beyond the candidate’s resume “in terms of why they are interested in the industry, what got them there, and what drives them.” But he cautions not to make any cover letter longer than two paragraphs.
Pramp CEO Refael Zikavashvili feels differently: “I would say that cover letters are antiquated, “ he says. “You don’t really see them often, and it’s not a practice I would recommend anyone to do.”
Stephanie Leuck, who is University Recruiter at 84.51 and who sees a ton of entry-level data analysis applications, says that while cover letters can help, it’s a dangerous game to play. “Maybe 1 in 20 candidates will have a cover letter,” she says, “and I don’t usually look at them.” When she does look, most candidates probably wish she hadn’t: “More often than not I see it actually hurt the candidate because it’s just too vague, they have the wrong company listed, or it’s just actually made me question: ‘Do you know what you want to do with your life?’”
In short: you can include a cover letter, and it can help you (with some recruiters). But you should be aware that it might not be seen, and you should also be aware that it can hurt you. In other words: if you’re going to include a cover letter, it had better be good.
Stephanie’s advice for cover letters? “Make sure it’s tailored to the company,” she says. “And make sure there is some valuable information in there. The idea is the cover letter is supposed to speak for you beyond what’s on your resume. Don’t repeat your resume for me.”
For entry-level applicants, she says, a good cover letter “could be explaining why you’re looking to make a career shift, or you were an English major in undergrad and then you pursued an advanced degree in data science or have done some work on the side through the different courses, and explaining how you’ve set yourself up for a career in data science. I think that’s a more valuable use of a cover letter.”
Ewa Zakrzewska, who’s an HR specialist and recruiter at Zety in Poland, says that when she looks at cover letters, she’s looking for applicants to “connect your measurable accomplishments with the job description and explain what you can bring to the table.”
“Saying that you have no experience but you are willing to learn on the job is probably not a good idea,” she adds.
Reaching Out Directly Instead of a Cover Letter
Another approach, and one that’s recommended by Pramp CEO Refael Zikavashvili, is reaching out directly via email to the lead data scientist, hiring manager, or recruiter in lieu of sending a cover letter.
“If the candidate proactively sends me an email and tells me, ‘I believe that you have this business challenge and this is how I would go about solving that,’ that will super impress me,” Refael says.
“Somebody who can actually identify problems based on public information and who will go the extra step and actually suggest a way to solve that, that will blow me away. That person gets an instant interview from me. No questions asked. I would even skip the resume at that point.”
This approach is discussed in more detail in the chapter on finding data science jobs, so please refer to the relevant section there for more information about how to execute this correctly. We will repeat here, however, that this is a high-risk, high-reward strategy, and what you send has to be very carefully tailored for your recipient and the company in question or it’s likely to hurt your chances rather than help them.
When to Actually Apply
As a data analyst or data scientist, you won’t be surprised to learn that the data suggest when you apply for a job matters. The same job application sent at 11 pm on a Saturday or 9 am on a Monday could produce completely different results.
Different data sets produce slightly different results, but the general message of most analyses are the same: You should apply between Monday and Thursday, ideally on Monday. And you should apply in the window between 6 am and 10 am. If you’re applying to jobs in other time zones, think about the recruiter’s time zone and when they’ll be seeing the application, not when you’re sending it.
Even the time of year may matter, although what time of year isn’t something you can always control. There’s no easy rule to follow, either — seasonal hiring patterns will vary by industry and by things like local holidays. For practical purposes, these differences may not matter much. If you see a job that interests you, you should apply regardless of the season. But it can still be helpful to learn about seasonal hiring patterns in your area so that if you see a dearth of jobs you know whether it’s just a seasonal slowdown or whether there just aren’t many jobs in your area.
Am I Ready to Apply?
This is the final section of this guide that addresses what to do before clicking that “Apply” button, so let’s address a big question many applicants struggle with: am I really ready for this job?
It’s certainly possible that you’re underqualified. If a job requires Python and SQL skills and you have neither, it’s probably a good idea to put some more time into studying before you spend any time writing a resume or filling out an application.
But it’s also important to keep in mind that impostor syndrome is real. One piece of advice that Dataquest students who’ve gotten data science jobs frequently share with us is that students should apply before they feel totally prepared. Carlos, for example, decided to apply for jobs on a whim after finishing just half of our data scientist path. He didn’t really think he was ready. To his surprise, he ended up with three different full-time job offers.
Not every job application ends with an offer, of course. Statistically, even great candidates are likely to get more rejections than they are interviews. It’s important not to take these rejections to heart, and to remember that you don’t have an awful lot to lose by applying. The worst that happens is you don’t get a job that you already don’t have.
Plus, even if you don’t get the job, you’ll often get valuable feedback that’ll help you improve your next job application, or figure out what skill you need to add to your toolkit to make yourself a more competitive applicant. Every application you send out, regardless of the response it gets, gives you another data point that helps you more accurately assess your own position in the job market.
That’s why we say: when in doubt, apply. You don’t have much to lose, and you might be surprised by what you’re ready to do!
This article is part of our in-depth Data Science Career Guide.
- Introduction and Table of Contents
- Before You Apply: Considering Your Options
- How and Where to Find Data Science Jobs
- How to Write a Data Science Resume
- How to Create a Data Science Project Portfolio
- How to Fill in Application Forms, When to Apply, and Other Considerations — You are here.
- Preparing for Job Interviews in Data Science
- Assessing and Negotiating Job Offers