The Data Science Interview Study Guide – The time is now more than ever to begin a career in Data Science. Data Scientists are among the most lucrative IT jobs, with a median annual income of roughly $100,000. This decade, there will be a 30% rise in the number of data scientists employed.

Before making that six-figure salary, you must be successful in your Data Science interview. Additionally, this interview requires more from you than just demonstrating your technical proficiency.

You may use the information in this post to better understand what to expect from your Machine earning interview and how to prepare for it. To find out how to ace the interview & get the job of your dreams, keep reading.

Are Interviews in Data Science Difficult?

Are Interviews in Data Science Difficult?

It is similar to preparing for other job interviews to prepare for a data science interview. You won’t encounter anything that your colleague’s Software Engineers haven’t encountered since it is even more comparable to interviews for other IT professions.

However, there are some peculiarities. Consider what you should be prepared for and how to react appropriately.

Where To Begin with Data Science Interview Preparation?

1. Study The Company and The Role

This is maybe the most important component of interview preparation for Data Science. Candidates who have tried to learn about the business and understand how Data Scientists may assist will stand out to recruiters.

The most obvious place to start your research is on the corporate website. It will provide a summary of the organization’s activities and objectives. If the website includes a blog, look at some of the most recent posts. By doing this, you may learn more about the company’s objectives and focus areas.

You may then carry out further in-depth research after that. Visit the company’s LinkedIn page to get a feel of its social media involvement. You should also follow or interact with certain key decision-makers since this will give you an idea of the business culture and its expectations for employees.

2. Examine Your Resume and Previous Projects

Even if it isn’t enough to earn a job, your data science portfolio may get you an interview. Use the same respect you would for your CV while handling your portfolio. Showcase your portfolio’s most relevant work while tailoring it to the business you are applying to.

Look at some of these data science projects if you don’t have anything to include in your portfolio or want to expand it.

Taking part in competitions on websites like Kaggle is a great way to build a powerful portfolio. In other instances, recruiters have just gotten in touch with data science hopefuls after looking at their Kaggle profiles. Due diligence should be taken while updating your Kaggle & Github accounts.

3. Refresh Your Memory of Foundational Ideas

You’ll need to show off your technical expertise to ace your data science interview. Technical recruiters want to see that you have a firm grasp of the fundamentals, whether you are just starting or seeking a senior-level post.

To be prepared for your data scientist interview, you should study the following technical concepts:

  1. Statistics of Probability
  2. Testing Hypotheses
  3. Bayesian and Descriptive Statistics
  4. Decrease in Dimensions

4. Develop Your Technical Skills and Get Ready for The Test

You should be able to show off a few technical talents during your Data Science interview. These consist of the following:

Analytical Statistics

The majority of free Data Science courses use computing to represent mathematical concepts. So, if you want to work in data science, you must have a firm grasp of mathematical and statistical ideas.

Utilizing Data

Data scientists need to be skilled at dealing with data. However, hiring managers want to know that you are familiar with and confident working with the complete data science process.

The first step in dealing with data is to obtain it. You must thus show in your data science interview that you can select data sources and extract the necessary data.


All data scientists don’t need to have programming experience, but it is quite beneficial to know how to code. Suppose you’re starting with data science programming; Python and Rare are fantastic options. You can swiftly improve your abilities by visiting websites like Kaggle, where you may participate in programming challenges.

Visualization and Modeling

A key component of a data scientist’s work is presenting their results. To do this successfully, you must be able to communicate technical ideas to non-technical stakeholders, put your results into simple language, and make use of charts and visualizations.

The Salary Debate of Data Science Interview

The Salary Debate of Data Science Interview

You’ll be questioned about your wage expectations at some point throughout the data science interview process. If you’re concerned that this may be uncomfortable, have a look at these practice questions:

What Salary Expectations Do You Have for This Position?

A smart place to start is by mentioning your past salary since it will help companies understand your background and your aspirations. Then you may expand on this and explain why you deserve to be paid more.

Here is the current pay scale for our data scientists. Do You Find It Effective?

Suppose you can work with the number they provide; that is wonderful. Ask if there is space for them to move if there isn’t. Present your case. Inform them of your reasons for deserving a higher salary and convince them that doing so would eventually result in cost savings.

Are You Willing to Accept a Lower Salary Than Your Previous Position?

In some circumstances, particularly if you’ll be working fewer hours, you could be receptive to such an offer. However, if you are unwilling to accept a lower salary, it is acceptable, and recruiters will value your candor if you kindly inform them of this.

Inquiries to ask the interviewer

Employing managers may tell that you have done your research on the company and thus are interested in the role if you have a few questions prepared for the interviewing. You might inquire about average workweeks, duties, organizational structure, chances for advancement, or almost anything. Just be careful not to pose a query that has a simple, online-accessible solution through free online courses.

Practice interviews

Practicing with a mock interview might make you feel more at ease and learn the technical abilities you need to improve. Invite a buddy, co-worker, or mentor to practice alongside you.


It might be challenging to interview for a new data science position. You must be prepared to demonstrate your fit with the organization, answer questions about your conduct, exhibit competence and initiative, and more within an hour or so, in addition to knowing the technical aspects of the work. Don’t forget to send your interviewers a thank you message afterward and to haggle over your pay and perks if you get an offer.