Top Data Science Interview Questions and Skills to Know

Let’s say you’re interested in a career in data science, and you manage to land a job interview at a firm that wants your data-crunching expertise. What kind of questions will you face? What sort of skills and abilities should you emphasize in your answers?

(For those unaware, a data scientist is responsible for understanding and aggregating datasets, and employing statistical and machine learning techniques to create predictive analytics and models. They work with data and application engineers to integrate these models into a software platform or product; alternatively, they use the data to help identify opportunities to improve organizational efficiency and increase business value.)

The field of data science is broad, and if you’re sitting across the desk from a potential employer, its important to know exactly what the company is looking for, and how your skill set as a data scientist is going to add value to the organization.

Steve Donahue, principal recruiter at Entrust Datacard, said data science candidates should consider themselves like a puzzle piece. How do you fit into the “puzzle” of the company where you’re interviewing?

As he noted, not every company uses the same tools, and you may bring something to the organization that they didn’t even know they needed. In light of that, it’s important to own what you have done and be proud of it.

“When we recruit a data scientist, we’re looking for experience with specific statistical modeling tools such as R, SciKitLearn or TensorFlow,” Donahue said. “For technical roles, we like to get into the nuts and bolts of the candidates’ previous roles.”

If you’re interviewing for a data science role, you must prepare for those kinds of technical questions, as well as your previous experience. For example:

  • How you can build a predictive model in the absence of labeled data (using unsupervised ML techniques, or keyword-based approaches to generate labels)?
  • What did you do on a daily basis? What did you do on the team?

For a full breakdown of the necessary skills for data science, let’s turn to Burning Glass, which data-mines data scientist job postings for insights into what companies want. Specifically, it divides skills into three categories:

Necessary skills are the lowest barrier to entry; they are also skills that are often found in other professions, providing a springboard for people to launch into a data-science career. 

Defining skills are the skills needed for day-to-day tasks in many roles.

Distinguishing skills (advanced skills called for occasionally) that truly differentiate candidates applying for various roles. As you might expect, there’s a lot of education and training necessary to master these.

With that in mind, here’s the breakdown:

Openness About Data Science Challenges

It’s important to discuss the problems and challenges (and there are many) that inevitably come with data science, especially when wrestling with huge and messy datasets. “It’s also said it’s important to know how a candidate handles challenges as well as success—we all know that not everything goes as planned in development,” Donahue added.

For example:

  • What do you do when your project is failing?
  • What are some alternative approaches or alternative statistical models you could make?

With that in mind, walk into the interview with a few stories of how you confronted (and overcame) a data-science challenge.

Ideally, the interviewer’s questions will be designed to reflect the nature of the work you’ll actually be performing at the company, and the kinds of data you will be dealing with. If the organization wants someone who can comb through some extraordinarily messy, unstructured datasets for strategic insights, for example, you might want to talk about how you dealt with something similarly messy at your old firm.

In addition to simply answering questions, you may be asked to demonstrate your skills. For example, you may be given a file containing mock data about traffic to different landing pages of your website, and asked to build a model that predicts conversion rates. It’s worth reviewing how to best use your tools and techniques before heading into the interview, especially if you know a test might be coming.

Radu Miclaus, director of product AI and cloud at Lucidworks, said more than any particular solution itself, interviewers are looking to see if you ask clarifying questions about the data, state the assumptions you’re making, and explain your thought process as you work through the problem. In other words, your thought process counts just as much as the solution.

“When we hire, we have to find the sweet spot between technology skills and business acumen,” he said. “We’re looking for people who can solve business problems, so we’re interested in how they screen the data and present the results. Understanding the methodology you’d go through, the process of how they think through the code, how they document the code.”

 Questions that might relate to this include:

  • What are some of the data sources you would use?
  • How are you going to acquire data, and what’s the end goal?
  • How do you structure the entire project around those considerations before adopting a methodology?
  • Can you take the problem back and think about it more holistically?

“Data scientists have to operate in an agile fashion—they have to think about version control, understanding the process of organization for maintaining code, and for documentation,” Miclaus said. “Data scientists are not decision-makers; they are providers of insight. It’s not enough for you to produce a model; it has to be deployable and implantable.”

So, going into an interview, it’s crucial that any data-science candidate understand the backdrop of the methodologies mentioned in the job posting, as well as how those are applied to achieve business goals. For example, if the company is focused on mapping, or the Internet of Things (IoT), the candidate will absolutely need to know how methodologies apply to those particular segments.

And it’s not enough to walk into the data science interview with some generalized data-science concepts; you need to “lean in” to whatever specialization the company is asking for. If that specialization is already your area, that’s great! Otherwise you may have a lot of homework before the interview itself.

Data Scientist Soft Skills

Finally, job candidates in data science can expect to field questions related to their commutations abilities and teamwork skills, Donahue said. As much as data scientists are prized for their numbers-crunching (and predictive) abilities, they also need the “soft skills” that will allow them to work in teams and communicate relevant results to the proper stakeholders.

“I ask how and when they ask for help,” Donahue added. “This shows how us they think and work as part of a team, and take advantage of people around them who can point them in the right direction toward solutions. When working on highly successful teams, splits on the direction of a project are going to happen.”

Here are some examples of “soft skill” interview questions:

  • How do you get all team members on the same page?
  • Do you have experience working in cross-functional teams?
  • Do you understand collaborative processes?

Data science is a hot field right now, with employers hungry for technologists with the right combination of data-crunching and strategy-predicting skills. According to data from Glassdoor, “data scientist” is the highest-paying entry-level job in the U.S. this year, with a median base salary of $95,000. But in order to land the job—and earn that kind of salary—data scientists will need to demonstrate that they have the ability to get the job done, as well as the soft skills to interact with teammates and sell their conclusions to their broader organization.