How to perfect yours to land a job interview

Data scientists have become increasingly crucial players in organizations, providing the information needed by leaders 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.

Due to the technical nature of their work, some data scientists find it difficult to write an effective CV. What skills and experiences should they highlight? What do hiring managers and recruiters really care about when data scientists apply for a job? We’ve spoken to several hiring managers to find out what it takes to write a data scientist resume that stands out from the crowd.

What do recruiters look for in a Data Scientist CV?

Lyndsey Padden, vice president of data science and 84.51 degree talent strategy, told Dice: “I’m a bit old school and really looking for a well-organized resume and well formatted where a candidate succinctly expresses what he has worked on. “

In other words, data scientist candidates should do their best to explain their (often complicated) experience in the simplest way possible. When writing your resume, you might even want a friend or colleague who isn’t a data scientist to read it. scientific expert.

Candidates with little data science background need not worry, Padden adds. “For someone early in their career or for someone new to data science, it can be helpful to see relevant courses or experiences where working with data and problem-solving have come into play,” she says. . “For more experienced professionals, references to projects and areas can be helpful. It’s also great to see the skill levels in different programming languages ​​and platforms. Ultimately, regardless of experience level, our organization is looking for people who are collaborators, learners, and proven problem solvers.

AI consultant Eugene Rudenko advises novice data scientists to focus on education first. “It is also essential to determine, from a junior candidate’s curriculum vitae, the expertise and resources he has before training,” he says. “If there are runtimes in CVS (GitHub, GitLab, etc.), we inspect the code and, during the interview, ask the candidate why they adopted a certain technique.

Along with seasoned data scientists, Rudenko adds that the job change can be a red flag for hiring managers. Several jobs over the course of a year or two may suggest a lack of skills or that it is difficult to work with you.

Matt Williams, founder of Snarful Solutions Group, says he’s looking for indicators that the person applying for the job may not be qualified, or even a data scientist. “A lot of people claim they are data scientists, but in reality, they were just the one data analyst in a small organization who was the go-to person. For a true data scientist, I’m looking for real, relevant work experience with complex data models and sophisticated use of math and statistics.

When applying for data-related jobs, it’s always helpful to remember that while the term ‘data scientist’ is often used interchangeably with ‘data analyst’, they are actually very different roles, with data analysts often focusing much more on tactical issues. than data scientists. In contrast, data scientists often take a more holistic and strategic approach to an organization’s data.

Technical skills matter, Williams adds, “Considering that the term ‘data scientist’ is now overused, you need to be sure that your technical skills stay current. “

Nate Tsang, founder of WallStreetZen, tells Dice: “I prefer to see the portfolio on a personal website. Your resume and personal website don’t have to be the same, but they complement each other. Indicate your experience with Git in the tech skills section of your resume, but your actual GitHub profile should be listed elsewhere.

Others don’t think a portfolio is absolutely vital, as long as your application materials showcase your skills and experience. Padden says, “I don’t think it’s necessary for all roles, but our hiring managers can read carefully if it’s provided.

But this point of view is not shared by everyone; for example, Williams thinks portfolios matter ‘absolutely’: ‘Just as you wouldn’t hire an actor for a great film based on a portrait and an interview, you shouldn’t hire a data scientist based on a portrait and an interview. curriculum vitae and an interview. Past successful work that can be shared and highlighted is extremely helpful in applying for jobs. (As for GitHub profile links? Williams says they’re valuable, but not essential for a data scientist resume.)

What makes an interview for a data scientist?

The purpose of a CV is to gain enough attention to land a job interview. With data science becoming an increasingly popular position, it’s sometimes not easy to stand out from the crowd either.

All of our experts agree on formatting issues, so be sure to pay attention to the layout of your resume. And when it comes time for a job interview, make sure you can talk in detail about the skills or experience you’ve listed. As Tsang puts it, “You listed your skills and experience on your resume – if you’re mumbling generalities about RStudio rather than explaining its use on a recent project, then you’re not following through. The curriculum vitae and what you want to to chat in the interview must be connected.

Padden agrees that communication skills are essential during the interview process – without it you can’t hope to land the job: “I don’t think there is necessarily a silver bullet. I really want to see someone who can clearly articulate what they’ve been working on in general industry terms. I like to see clear evidence of the application of academic techniques to solve business problems and of a personal commitment to learning.

Soft skills are just as crucial in a job interview as they are in the job itself. Having a grasp of a technology, language, or discipline is great, but make sure you can discuss these things in a way your interviewer understands, regardless of their data science expertise. Practice your answers before your interview.

Sean N. Ayres