Entry-Level Job Application Tips from a Data Science Recruiter
There’s no doubt that data scientists are in high demand. But the dearth of data scientists at higher levels, in combination with the “sexiest job” media hype, have attracted a lot of new blood to the profession. That’s caused a bit more competition than you might be expecting when it comes to entry-level positions.
So how can you get an entry-level job in data science? We’ve already shared some great tips from data scientists, but to learn even more, we spoke to Stephanie Leuck. As the University Recruiting Manager at data analytics firm 8451, Stephanie spends a ton of her time handling entry-level data science job applications, and she has seen thousands of them.
We asked her to share some insights on how entry-level data science applicants can stand out from the herd. Here’s what she told us:
Showcase Success With Hands-On Projects
Since entry-level applicants typically aren’t going to have any job experience, Stephanie said she’s looking for the next best thing: evidence of success doing data-science-related work.
“I’m looking for examples of projects that get at different skill sets,” she said. “You know, statistical projects or data science oriented projects, business analytics.” Specifically, she likes to see projects that demonstrate applicants “have the critical thinking and problem solving that a data scientist needs, as well as some of the coding experience.”
That doesn’t mean she wants to see code on your resume, but she does want to see evidence of coding skills. Projects you showcase should demonstrate you understand the kinds of problems data scientists need to solve, and they should showcase that you know how to get your hands dirty with SQL, R, or Python to actually solve them.
Projects you showcase should also probably be unique. Seeing common projects doesn’t hurt entry-level applicants with her, Stephanie says, because all practice is good practice. But she’s aware that there are dozens of tutorials for many of these projects online, so they’re not likely to impress her because she knows you may have just followed a tutorial.
“If [your project] is a more original idea or original thought, that’s going to add more weight,” she said.
Be Specific On Your Resume
One way entry-level applicants can make a recruiter’s eyes glaze over, Stephanie said, is with vague descriptions of projects that don’t make it clear what the applicant actually did. Descriptions like ‘I used statistical methods to analyze…’ or ‘I applied data science techniques to the data…’ don’t give the recruiter any real information, and they suggest you might not really know what you’re doing.
Instead, Stephanie said, you want to be clear about what you’ve done. This doesn’t mean you need to include every minor detail, but if you’re describing a project, you probably want to include the coding language and specific statistical methods you used in the description.
So, for example, instead of saying ‘I used statistical methods to analyze…’ you should say something like ‘I used R to do Poisson Regression and analyzed [my project topic].’ This tells the recruiter that you have experience with this specific language and this specific statistical concept. You may even want to include details about specific packages or libraries you used if they are common in data science work.
You Need a GitHub
Without job experience, showcasing your projects is crucially important, at a minimum, you need to have a GitHub. “I think a GitHub is essential,” Stephanie said.
“A GitHub or website is a great resource because it’s easy to access,” she said. “It’s something you should put on your resume, but make sure that it has valuable information on it and it’s up to date and it’s active. Otherwise it can hurt you.”
At 8451, Stephanie said, they’re not looking for particular types of projects on GitHub, because the company handles a wide variety of data science work. So instead, she said they “look for folks who are going to be more of that of passion person, a life learner.”
“I’d rather see that [applicants] were just dabbling in the different languages, like R or Python, or even some of the more computer science oriented languages,” she said, “and just practicing and building and testing.”
Whatever you include, Stephanie said, make it good and don’t take GitHub for granted. “I think GitHub is an amazing tool that basically lets you show that you can do the work without actually having a job. There’s not a lot of industries where you can do that.”
Cover Letters: Do It Right or Don’t Do It
Most data science applications don’t include a cover letter; Stephanie estimated that just one in twenty of the applications she sees comes with one. And depending on when that application comes in, it may not even get looked at: “If we’re in the fall and we’re looking at thousands of resumes, I don’t have time to also look at your cover letter.”
There may be some good reasons to submit a cover letter, particularly if you’re moving into data science from a different career or coming into data science after graduating with an unrelated degree. The cover letter can be a good place to explain that, Stephanie said, if you can do a good job of showing how you’ve set yourself up for a career in data science.
But it’s a dangerous game to play: “More often than not I see [a cover letter] hurt the candidate because it’s just too vague, they have the wrong company listed, or it has actually made me question: ‘Do you know what you want to do with your life?’”
“If you submit a cover letter,” she said, “make sure it’s a good one. Make sure it’s tailored to the company, make sure it has 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.
Prove You Know How to Solve Problems
Employers aren’t expecting entry-level applicants to have total mastery of every possible technical skill, Stephanie said, so if you’ve got a solid command of either Python or R and SQL, you probably have all of the programming skill you need to get a data science job.
But beyond the technical skills, you also need to have an understanding of the statistical methods you’re using and how they can be applied to solve actual problems. If you know how to use a linear regression model in your code but you don’t know when or why to apply a linear regression model, that’s a problem. You need to be able to think critically and assess what the problem is and how to solve it before you can apply your technical skills.
“We can teach you how to code,” Stephanie said, “but you need to know how to think.”
This can be difficult to demonstrate on a resume, but one way to approach it is to showcase projects that have addressed business problems, and briefly explain what the problem was and how your project helps to solve it. You can go into far more depth about this sort of thing on your Github, too. Having nicely-formatted descriptions of what business problem your analysis addresses, why you’ve applied the techniques you did, and how your results solve that problem can help demonstrate that you’re not just coding, you’re using your coding and statistics skills to identify and solve real problems.
(Don’t have the coding skills or the statistical knowledge you need to pull that off yet? Dataquest can teach you to code in Python or R from scratch, and we also teach how and more importantly when and why to apply various statistical and machine learning methods).
Soft Skills Matter
“We don’t want somebody that just sits here behind a computer and codes,” Stephanie said. “We need somebody who’s going to be a contributing team member.”
“We work in teams,” she said of 8451. “We’re a heavily collaborative work environment here, so the ability to work on a team with a shared vision is really essential. Part of that is being open to feedback and being receptive to feedback, and also being willing to offer constructive criticism when needed, like challenging the status quo.”
While she was speaking specifically about 8451, most companies are going to want to see communication and teamwork skills in entry-level applicants for any position. That’s particularly true for data analysts and data scientists, because data jobs typically involve frequent contact, communication, and collaboration with other teams. Even if the data science team itself is small, in the space of a year it’s not uncommon for a data scientist to have worked with numerous other teams across their company to collect and analyze data, build dashboards, make predictions, etc.
So how can you demonstrate that you actually have those skills on your resume? “We like to see people that are involved things outside of just their coursework,” Stephanie said. “That can be student organizations, networking organizations, community organizations. Certainly it helps if they’re data science oriented or technically oriented. Participation in case study competitions, hackathons… these are all great things to showcase on a resume because it tells us that you most likely worked in a group.”
Team projects are another good way to showcase these skills. Stephanie said she’s excited to see team projects on a resume, because “that gives me a springboard to say ‘So, who were you on that team? What was your role? How would your teammates have described you?’ And I can start to get to some of those soft skills to get an idea of whether you are a team player.”
But she also issued a warning: don’t be the person that says: ‘Yeah, everybody on my team, nobody did the work and I had to lead it all.’
“That gives me a bit of a red flag,” she said.
If you’re hoping to get a call back for an interview, your job application for any position needs to stand out from the herd. All of the tips above should help with that, but if you’re looking for a big takeaway that sums everything up, it’s this: for entry-level positions, your job application should show that you can do the work.
Without work experience to assess, a recruiter’s first task is going to be to figure out whether or not you have the skills needed to do the job. You need to make sure that your resume tangibly demonstrates that you have those skills.
- Showcase projects to demonstrate your programming and data science skills, and be specific in describing what you’ve done.
- Have an active Github that shows you’re doing data science work regularly, and that you understand the context of the work you’re doing and how it can help solve business problems.
- Participate in meetups, hackathons, and group projects to demonstrate teamwork and other soft skills.
If you can get these aspects of your application squared away, you’re putting yourself in a great position to stand out from the pack.
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