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Data science is having a moment: 73% of data science and analytics teams were hiring in the first half of 2021, with 81% for the remainder of the year.
There are several definitions of “data scientist,” and each company has an idea of what a data scientist can do for the organization. Unfortunately, that means there’s no such thing as a standardized list of data science engineer interview questions to expect. And since data science interview questions can encompass a broad range of material, it’s tough to target interview preparation for a specific role you’ll play within an organization.
The good news is that there are some common characteristics of data science interview questions that you’re likely to see in an employer’s quest to determine aptitude and fit – and knowing them will put you a step ahead of other candidates.
This post outlines the general types of data science interview questions you can expect, drawn from our conversations with current data scientists and discussions on popular online forums for data scientists.
What projects are you most proud of?
This data science interview question is designed to help employers understand the depth, complexity, and effectiveness of projects you’ve worked on.
The type of projects you can expect to discuss depends on the kind of role you’re interviewing for. For example, showcasing a GitHub repository is better for a programming-heavy job, whereas a paper might be better suited for a research-heavy role.
Candidates who are fresh out of school should have either an independent project to show and those that don’t can produce a one-pager that maps their journey from the problem (the origin) to the solution (the destination).
Prepare for this question by practicing a brief explanation of the project’s problem, how you designed a solution, how you implemented it, and the final results. This will offer insight into how you reason, approach problems, organize information, and streamline processes. Expect to answer follow-up questions about your choices in approach.
How would you sort an array that has the following strings?
This is an example of an abstract question data science candidates can expect during an interview, particularly if the role involves a lot of programming.
Typically, interviewers will present a scenario in which data comes in a specific form and asks how to classify it. These types of questions assess knowledge and creativity and will likely match the company’s interests. For example, data scientists interviewing with a bank might be asked how you’d use a table to get to an algorithm that approves or denies loans – in contrast, another industry might ask about Natural Language Processing.
A good interviewer will not ask for you to provide a final solution to a highly complex, super-niche scenario; in reality, nobody can give a good solution right there on the spot without research and analysis.
This type of question should warrant a back-and-forth with the interviewer, who will assess you based on the exploratory questions you ask to evolve your ideas.
In this scenario, would you implement a Z-test or a T-test?
Data engineer interview questions will definitely include statistics-based questions, so brush up on theorems, rules, and correlating applications.
In this example, a Z-test is speculative and used when you have less of a sense of populace change but have a huge data sample, whereas a T-test would be used with a smaller data set – and they’ll want to see if you answer in a way that reflects you understand that.
Consider data science interview questions an employer might ask that are relevant to the role and company; for example, if you’re working within machine learning, they may ask you about Bayes Theorem and correlating techniques.
Tip: Customize your data science interview preparation by doing some digging online. Sites like Glassdoor, Facebook groups for data scientists, and Reddit/data science have real questions employers asked candidates during interviews.
How are your skills transferable to our company?
While this data science interview question is most likely to be asked of a candidate who is just out of academia, it’s not uncommon for data scientists to move among industries. After all, the principles remain the same whether you’re working for a pharmaceutical company, a clean energy company, or a research institution.
In any of these cases, the employer will want to know how to apply your previous experience to what they need.
Before your interview, get familiar with what the company is doing and think about where data scientists may play a role in moving (and improving) that mission forward – and how it connects to what you’ve done already. If there are some small knowledge gaps that would prevent your skill from being transferrable comfortable, sign up for a MOOC or utilize other professional development resources.
Bonus question: when can you complete a coding test on our online interviewing platform?
Data science engineer candidates will likely encounter this early on in the interview process because it is an effective way to assess skills. Having strong programming skills is essential because data scientists can’t implement their ideas unless they can build them.
At Codility, we have a library of tasks that assess data science skills; they include coding (like Python, R, and SQL tasks), data manipulation, machine learning, business analytics, data visualization, and statistics – some in multiple-choice form.
Because of COVID and the rise of remote work, more employers opt to issue our data science tests virtually. Make sure you know what to expect and prepare accordingly.
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