Data scientists have become increasingly crucial players within organizations, providing the insights necessary for executives to make strategic decisions. Not only do successful data scientists possess highly specialized technical skills—they must also master “soft skills” such as empathy and communication in order to effectively share their work with all types of stakeholders.
Given the technical nature of their work, some data scientists have trouble crafting an effective résumé. What skills and experience should they highlight? What do hiring managers and recruiters actually care about when it comes to data scientists applying for a position? We spoke to several hiring managers to find out what it takes to craft a data scientist résumé that stands out from the rest.
What do Hiring Managers Look for in a Data Scientist Résumé?
Lyndsey Padden, Vice President of Data Science and Talent Strategy at 84.51 degrees, tells Dice: “I am a bit old school and am really looking for a well-organized, well-formatted résumé where a candidate articulates what they have worked on succinctly.”
In other words, data scientist candidates should do their best to explain their (often complicated) experience in as streamlined a way as possible. When writing your résumé, you may even want a friend or colleague who isn’t a data scientist to read it—if it seems clear to them, it’ll also be clear to a hiring manager or recruiter who isn’t a data-science expert.
Those candidates without much data-science experience needn’t worry, Padden adds. “For someone early in their career or new to data science, it can be helpful to see relevant coursework or experiences where working with data and problem-solving have come into play,” she says. “For more experienced professionals, references to projects and domains can be helpful. It’s also great to see levels of proficiency in different programming languages and platforms. At the end of the day—regardless of experience level—our organization is looking for people who are proven collaborators, learners and problem-solvers.”
A.I. consultant Eugene Rudenko advises junior data scientists to highlight education first. “It’s also vital to determine from a junior candidate’s résumé what expertise and resources he or she holds before training,” he says. “If there are any runtimes in CVS (GitHub, GitLab, etc.), we inspect the code and, during the interview, ask the applicant why he or she adopted a certain technique.”
With senior data scientists, Rudenko adds that job-hopping can be a red flag for hiring managers. Multiple jobs over the course of a year or two may suggest a lack of skills or that you’re difficult to work with.
Matt Williams, founder of Snarful Solutions Group, says he looks for indicators the person applying for the job may not be qualified, or even a data scientist. “Many people claim they are a data scientist, but really they were just the only data analyst in a small organization that was the ‘go to data person.’ For a true data scientist, I am looking for real, relevant work experience with working with complex data models, and a sophisticated use of math and statistics.”
When applying for data-related jobs, it’s always helpful to remember that, although the term ‘data scientist’ is often used interchangeably with ‘data analyst,’ they’re actually very different roles, with data analysts often focusing much more on tactical problems than data scientists. By contrast, data scientists often take a more holistic, strategic approach to an organization’s data.
Technical skills matter, Williams adds: “Considering that the term ‘data scientist’ is now being overused, you need to be sure that your technical skills are staying current.”
Do Portfolios and GitHub Profile Links Earn You Interviews?
Nate Tsang, founder at WallStreetZen, tells Dice: “I prefer to see the portfolio on a personal website. Your résumé and your personal website shouldn’t be one and the same, but they complement each other. List your experience with Git in the technical skills section of your resume, but your actual GitHub profile should be listed elsewhere.”
Others don’t think a portfolio is absolutely vital, provided your application materials highlight your skills and experience. Padden says: “I don’t think it’s necessary for all roles, but our hiring managers may peruse if provided.
But that view isn’t held by everyone; for example, Williams thinks portfolios “absolutely” matter: “Just as you would not hire an actor for a large movie based on a headshot and an interview, you should not hire a data scientist based on a résumé and an interview. Past successful work that can be shared and highlighted is extremely useful when applying for jobs.” (As for GitHub profile links? Williams says they’re valuable, but not critical for a data scientist résumé.)
What Gets a Data Scientist an Interview?
The point of a résumé is to attract enough attention to land a job interview. With data science an increasingly popular position, sometimes it’s not easy to stand out from the crowd, either.
All of our experts agree formatting matters, so be sure you’re mindful of your résumé layout. And when the time comes for a job interview, make sure you can talk in great detail about any skills or experience you’ve listed. As Tsang says: “You listed your skills and experience on your résumé—if you mumble generalities about RStudio rather than explain its use on a recent project, then you’re not following through. The résumé and what you want to discuss in the interview should be connected.”
Padden agrees that communicating skills is critical during the interview process—without doing that, you can’t hope to land the position: “I don’t think there is necessarily a magic bullet. I really want to see someone who can cleanly articulate what they have worked on in general industry terms. I like to see clear evidence of applying academic techniques to solve business problems and personal commitment to learning.”
Soft skills are just as crucial during a job interview as in the job itself. Having mastery of a technology, language, or discipline is great, but make sure you can discuss those things in a way your interviewer understands, no matter what their expertise in data science. Practice your answers before your interview.