Anastasia Cherepanova
Anastasia Cherepanova
Software Engineering Bootcamp
Career Product Lead
Ana Mineeva
Ana Mineeva
Career Product Lead

Congratulations! You’ve been invited for a data science interview. All that applications-submitting, networking, and portfolio-building has finally paid off! Now, let’s plan wisely for this interview, as it is an important step in landing your dream IT role. 

We’ve highlighted our best pre-interview recommendations below! Get familiar with these steps to ensure you enter this process with confidence.

Learn about the company’s hiring process 

The data science interview process often includes a series of interviews. Each one brings you closer to success, but also requires some preparation. It’s always beneficial to check out some resources and find out what to expect, as it increases your chances of getting a job offer. 

The hiring process can take anywhere between two weeks and 3-4 months and may consist of:

  • 1-2 rounds of phone screening
  • 4-6 onsite or online interviews 
  • online or take-home assessments 

The first interview is usually set as a primary screening to confirm the information you revealed on your resume, LinkedIn profile, and portfolio. Most often, it's done by a hiring agency or HR person within the company. 

The way the company makes you feel during the hiring process may be a good indicator of how it will feel to work for them and even if it is worth continuing. Possible red flags are ghosting, inconsistencies, and misleading information from different people within the company. Efficiently organized companies that value their employees will act expeditiously and provide feedback, even if you don’t get hired.  

Get familiar with different types of interviews

There are different types of interviews that one can expect when applying for a data science job. Let’s take a look at the most common ones.

Case Study Interview

Business case or data science case study interviews access your thought process in resolving real-world problems. How will you deal with ambiguity? Can you look at the problem from different angles and make optimal decisions? Are you demonstrating that you are able to analyze the business situation quickly and offer ways to improve products or address business problems efficiently? Will you be able to communicate your conclusions across different teams and people with diverse backgrounds? How a company sets up its case study interviews may vary from an on-site or online assessment to a take-home prompt. That's why it is essential to research the company and its current practices. 

Structured Query Language (SQL) Interview

There is a high chance that as a data scientist, you will use daily SQL to operate relational databases and to store and retrieve data. SQL interviews will test your knowledge of the language, syntax, and function, your ability to understand the entire data science process, and how quickly you can create efficient queries. Search the web for typical questions so you practice! Search the web for typical questions and use them to practice! 

Probability and Statistics Interview 

This type of interview tests your general knowledge. A solid understanding of applied statistics and probability is required for any data science job, whether designing A/B tests or making data-driven decisions. Brush up on the core statistics course from your bootcamp before going in for this interview. 

Machine Learning Interview

Questions may range from basic to advanced, depending on the position you are interviewing for. You can be tested for knowledge of algorithms and the theory of machine learning, how to compare algorithms and measure their accuracy and efficiency, and whether you can execute on top of it and apply your theoretical knowledge to company-specific problems. 

Coding Interview

Differing from an SQL interview mentioned earlier, a coding interview tests your ability to program in languages, such as Python. For this, be sure you understand computer science fundamentals, common algorithms, and data structures. 

Behavioral and Experience Interview

During this interview, a company wants to know if you can collaborate effectively and if you’re a good fit overall. Being friendly and attentive to details will help you nail it. Think about the questions and situations and allow for some spontaneity and freedom; let your personality show.  

Take some practical steps

Review the job posting and requirements. Carefully review the job posting and requirements. Make sure you understand the specific skills and qualifications the company is looking for in a candidate. Take stock of your experience and skills – most likely, you’re in the game. 

Brush up on your technical skills. Review the key concepts, technologies, and tools that are commonly used in data science, such as Python, R, SQL, machine learning, and data visualization. Be prepared to explain how you have used these skills in previous projects.

Prepare your portfolio. If you are asked to bring your portfolio, make sure it contains projects, data visualization, and other relevant materials demonstrating your data science skills and experience. It’s not necessary to design it in a particular way, but make sure it’s clear, professional, and highlights your experience. (The variety of cases you include could also pay off!)

If you are new to data science, you can build your entry-level portfolio based on your education, bootcamp, or real-life externship projects. It helps if you post feedback and reviews on your work. When in doubt, look up outstanding examples from other data scientists on the web or in your community.

Prepare questions to ask the interviewer. Before any interview, it is good to do some homework and find out basic information about the company: how old is the company, what type of products they produce, what kind of reviews it gets, and what are the latest statistics. This knowledge will also help you to prepare a list of questions to ask the interviewer. This will show your interest in the company and role.  

Starting in 2022, companies in some states are obliged to publish the salaries they offer for positions, so you may know the salary beforehand. But it would be also good to ask about the benefits of the position and whether it is higher or lower than the average for the specific location and market. It is best to assess your market worth and give the interviewer the salary range you are willing to work for. Prepare to negotiate and answer questions about your expectations.  

Think about your answers. Practice answering common interview questions, such as "Why do you want to work in data science?", "Can you give an example of a data science project you have worked on?", “What qualifications will help you in this position?”, or “What are some areas for improvement?” Prepare to speak on how you are planning to upend missing qualifications if there are any, and what can compensate for them. Sometimes, demonstrating enthusiasm for the job and willingness to learn means more to the interviewer than some of the missing experience.

Of course, some questions may be data science-related, so be sure to brush up on the basics to give confident answers. Here are a few examples:

  • What is logic regression and how is it done?
  • What is bivariate analysis? How is it different from univariate?
  • What tables are included in a SQL for customers’ orders?
  • What is K-means clustering?
  • Give five examples of the usage of decision trees. 

Practice in advance. Don’t wait to prepare for your interview once you receive a call. Getting ready for all six types of interviews with hundreds of possible questions and scenarios is not a simple task. Consistently devote some prep time for a few weeks and once you receive an interview invitation, research the company and briskly review your skills and knowledge. 

Your best strategy is to be fluent with all types of interviews and questions to adapt quickly and apply your knowledge accordingly!

Mock interviews are a fantastic way to reduce stress and make sure you present thes best version of yourself. Mock interviews imitate real ones, giving you the chance to prepare for any surprises and practice your soft skills. “Mock interviews are an important step in Practicum’s Acceleration Program,” shares Anastasia Cherepanova, Practicum Career Product Lead. “First, students practice answering interview questions with their personal coach and then they prepare for technical interviews with the support of Practicum’s technical tutors.” 

Ana Mineeva, Career Product Lead, adds that “Practicum's сareer coaches and tech industry leads ask the same questions they would ask on a real technical interview, thus helping students ace their interviewing skills.”

Dress professionally and arrive on time. First impressions are essential, so make sure you are punctual and dressed appropriately. This doesn’t mean you have to go out and buy a new suit; jeans, a blank t-shirt, and a casual jacket will do just the trick. If your interview is scheduled online, don’t forget to check your connection, microphone, and camera at least 10 minutes ahead of time. You can’t blame an application or service if something goes wrong. And you will struggle to create a good impression if your connection lags.

How Practicum can help

All Practicum’s bootcamps include access to the Career Prep and Career Acceleration programs. Within them, students receive a career services package, consisting of services like interview coaching that focuses on enhancing the quality of interview responses and task-solving skills. 

Practicum's Career Center will help refine your skillset so that you feel more confident going into real interviews and kickstarting your career in tech.


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