How to Find a Data Science Job (Advice from a NASA Datanaut)
You don’t have to be a data science veteran to find success in the field. Just ask NASA Datanaut Alyssa Columbus.
Although she graduated from college earlier this year, Alyssa already has a full-time data scientist role at Pacific Life. She also boasts a laundry list of speaking engagements and has founded a local group for women who code in R.
Oh, and did we mention she’s a NASA Datanaut?
We asked Alyssa what it takes to break into the industry. What she had to say may surprise you.
Here’s her advice regarding foundational skills, internships, portfolios, and the six-step recipe for a successful job hunt:
Build the Right Foundation
Many people are looking for a magic wand to instantly turn them into a successful data scientist. Despite her seemingly overnight success, Alyssa assures us there is no silver bullet. And there’s certainly no substitution for a solid skill set in data.
“Take some free online courses from platforms like Coursera, EdX, and MIT OpenCourseWare,” Alyssa said. Specifically, she recommended MIT and Harvard’s intro to computer science courses, MIT’s course on machine learning and statistics, and Stanford’s course on machine learning.
She also recommended Dataquest (We swear, we didn’t ask her to):
“I’ve taken a few Dataquest courses and found them very helpful,” she said. “I took Command Line and Git and Version Control; I’d also recommend that.”
Get Some Field Experience
Once you have the basic foundation, the next step is clear: Reinforce it with some on-the-job training. Alyssa says you can’t be afraid of internships.
“Internships and research positions may eventually turn into full-time job offers if you work very hard and show initiative,” she said.
“I worked very hard over time to exceed expectations at my internship at Pacific Life this summer, and that’s how I landed my full-time data scientist role. I also have previous internship experiences too; I had two or three internships in data analysis. That definitely helped me to gain more experience, skills, and expertise.”
Make Yourself Visible Online
According to Alyssa, skills alone are never going to get you a job. Potential employers have to be able to see your capabilities for themselves. And the best way for them to see this is online.
“If you really want to stand out,” Alyssa says, “Have a very strong online presence in the form of a website, portfolio, GitHub, blog, Kaggle profile, (or all of these) that showcases your interest, passion, and proficiency in data science.”
Advantages of an Online Presence
“Your strong online presence will not only help you in applying for jobs,” Alyssa said, “but employers and others may also reach out to you with freelance projects, interviews, and speaking engagements.”
In fact, the article you’re reading right now is an excellent example of that point. Dataquest reached out to interview Alyssa because we saw her posting cool things as the rotating curator of the R Ladies Twitter account. Putting yourself out there on the web can be scary, but it can also lead to unexpected opportunities.
What to Share Online
What exactly should you be posting to get attention from recruiters? Alyssa says you need a portfolio and some kind of website at a bare minimum:
“Get yourself a website if you haven’t already. It is essential for a data scientist to have a website and a GitHub,” she said. “GitHub Pages are free to host your website. You can even write your website in R, and I’d recommend Blogdown for doing that.”
Then, there’s the crucial portfolio.
What Makes a Good Data Science Portfolio?
Everybody knows that building a good portfolio is important for finding data science jobs. But the content of your portfolio really depends on the work you want to do in the future. Alyssa recommends researching the types of roles you’re looking for first. Then, plan portfolio projects that demonstrate skills relevant to those roles.
Pro Tip: Include 4 Projects that Show Your Depth and Range
Alyssa takes the guesswork out of developing a winning portfolio: “When you’re starting to build your portfolio, think about four projects that you’ve already completed or could complete quickly that show both depth of expertise and range of expertise.”
You’ll definitely want to include projects that highlight your coding skills in one of the top languages requested in data science job postings (i.e., Python, R, and SQL in that order). Make sure that code looks professional, Alyssa advises.
“Have clean readable code, use version control, break your project into multiple files. Comment, comment some more.”
She also recommends visualization or storytelling:
“Storytelling is a critical skill, along with communication.”
Your story could take the form of data visualizations or just a well-written write-up. The end goal is to present your data in a format that’s both compelling and easy to understand. Even with projects that aren’t focused on storytelling, Alyssa recommends including a write-up.
“Posting the code on Github is great, but it’s even better to have a write-up with it, whether it’s a readme or a blog post or something else,” she said.
She also recommends including a project that demonstrates your ability to deploy something, “whether it’s a RESTful API for a machine learning model you trained or a nice R Shiny or a Tableau dashboard.”
“If you’re looking to get more into data science, I’d recommend R Shiny,” she said. “If you’re looking to get more into business intelligence or data analyst roles then I’d recommend starting with Tableau, especially if you’re not comfortable with coding yet.”
Be Selective When Choosing Portfolio Pieces
Understand that recruiters will also be reading between the lines when evaluating your portfolio.
“Portfolios are also about what you don’t include,” Alyssa said. Your portfolio should show off your absolute best work, so don’t throw every single homework assignment into it. It also needs to be a reflection of your skills, so group projects should only be included if you made a significant contribution to them.
Also, you shouldn’t focus too much on simple data cleaning or exploratory data analysis. Those elements can be included as part of other projects, but they probably shouldn’t be the focal point of a project showcased in your portfolio.
Finally, your portfolio should demonstrate unique projects that you’re genuinely excited about. In interviews, you’re going to be asked about these projects:
“Your excitement will definitely make the interviewers more excited to hire you,” Alyssa said. “I’m speaking from experience, but also it’s common sense.”
Pro tip: Most of Dataquest’s data science courses contain a comprehensive and interactive project that you could add to your project portfolio. For example, if you finished our entire Data Scientist Path, you would have completed 26 total projects. Signing up is free.
How to Actually Find Entry Level Jobs (Hint: You’re Probably Doing It Wrong)
Once you’ve got the skills and the portfolio, it’s time to hit the tech job boards and start looking for cool jobs, right? Wrong.
Job Boards Aren’t the Answer (At Least Not Initially)
According to Alyssa, job boards should be the last place you look.
“When you’re job hunting, it may be tempting to look for work on company websites or check specific job boards, but according to people who are employed in the data science industry, including me, these are among the least helpful ways to find work,” Alyssa said.
Why is that?
“A lot of those turn into black holes if your resume doesn’t stand out in any way,” Alyssa said. And applying for jobs on web-based job boards is quick and easy, so your resume is going to be buried under a mountain of other applicants.
So what’s the right way to go about finding job opportunities?
Steps for Landing a Job in Data Science
Alyssa provides a specific recipe for landing your first data job:
“In order, I would look for work via recruiters, then friends/family/colleagues, then career fairs or recruiting events, then general job boards like LinkedIn, then company websites, and then tech job boards,” she said.
To summarize, here’s how Alyssa suggests you proceed with the job hunt:
- Career Fairs
- General Job Boards (e.g., LinkedIn)
- Company Websites
- Tech Job Boards
Tips for Total Newbies
But what if you’re a total newbie with no recruiter contacts or connections in the industry? First, you should work on fixing that by going to meetups and conferences and taking advantage of other networking opportunities. But Alyssa said that reaching out directly to recruiters you don’t know is okay too:
“I would reach out on LinkedIn. I would search ‘data scientist recruiter’ or something similar on LinkedIn and message them before asking to connect, or message them while asking to connect.”
The key is to remember that the job hunt is a two-way street.
“Don’t just message them ‘Do you have a job for me?’” Alyssa said. “Instead think of how you can make their job easier, searching for candidates you know they need. Study their company and see where their greatest challenges are and explain how you can help solve them.” That’s the quickest path to getting an interview, she said.
“Conveying your interest in helping their business grow goes much farther than immediately messaging them asking for an interview.” Alyssa said. “You get what you give, so show how you can make a contribution as best you can and hopefully an interview will follow.”
Five Key Takeaways
Here are five important bits of wisdom Alyssa shares. Keep these gems in mind before you file that next job application:
- Don’t look down on internships. Job experience is job experience, and they can lead directly to full-time staff positions.
- Put yourself out there, on the web and in real life. You never know what jobs or other opportunities might come to you, but nothing will come to you if you’re not visible on the web and actively participating in the community.
- Tailor your portfolio to the job you want and the passions you have. You’ll have to talk about a lot in interviews, so make things you’ll be excited to talk about.
- Stay off job board sites. You’ll get better results by building relationships with recruiters and leveraging connections in your network.
- Remember that relationships are a two-way street. Don’t just ask people for jobs; think about how you can help them out (even if it’s just buying them coffee).
Dataquest thanks Alyssa Columbus for taking the time to talk to us. You can learn more about her work on her website and follow her on Twitter.
And don’t forget to check out the Dataquest courses recommended by Alyssa!