9 Data Science Interview Questions and Answers for 2021

September 8, 2021

Data science is one of the fastest-growing fields of study in technology and uses multiple methods of extracting both structured and unstructured data to draw relevant conclusions. Employers in a variety of industries need professionals with data science experience and skills, including those in statistics, data analysis, machine learning and other related fields. In this article, we provide data science interview questions and example answers you can review to prepare for your interview and secure employment as a data scientist.

What is data science?

Data science is a discipline that organizes, analyzes and discovers insights from data to be used in making informed decisions. Companies and industries accumulate large amounts of data, and they use the results in multiple ways to determine the best ways to improve their business processes. For example, organizations can gather customer feedback from multiple platforms and get valuable information on the needs and desires of their customers. They may use this information to revise their marketing strategy or create a new product.

Being such a diverse field, applicants for data scientist positions need to have strong technical knowledge in fields such as mathematics or computer science, but also possess soft skills like the ability to work under pressure and good communication.

Common data science interview questions

Here are nine of the most frequently asked data science interview questions:

  • Why do you want to work at this company as a data scientist?
  • How did your previous work experiences prepare you for a role as a data scientist?
  • How do you overcome any professional challenges?
  • What tools and devices do you plan to use in your role as a data scientist?
  • What is selection bias, and why do you need to avoid it?
  • How do you organize big sets of data?
  • Is having large amounts of data always preferable?
  • What is root cause analysis?
  • How do you usually identify outliers within a data set?

1. Why do you want to work at this company as a data scientist?

This question allows you to describe what interests you in data science, the specific job listing and the company as a whole. You can demonstrate your passion for technology and analytics or your interest in utilizing big data to achieve company goals. You can also state that you are specifically interested in the way that particular company gathers and analyzes large amounts of data.

Example: “I have a degree in computer science and a passion for solving issues by processing and analyzing data. That’s why I am looking for a forward-thinking and data-driven company that has a rich history of using data to improve the quality of its products. I’m eager to serve in a position that allows me to achieve my career goals while excelling at work I’m passionate about.”

Read more: Interview Question: “Why Do You Want to Work Here?”

2. How did your previous work experiences prepare you for a role as a data scientist?

The diverse skill set required for this position may require you to demonstrate relevant experience in both technical skills and interpersonal communication. The best way to describe how your previous experiences prepared you for a role in data science is by using the STAR interview response technique by describing a situation, talking about what your task was in that particular context, discuss the actions you took to complete the task, as well as the results of your actions.

Example: “My previous job was for a tech company where I gathered customer feedback on their applications from multiple platforms and filed monthly reports to management, outlining my findings. My main task was to find common issues that applied to most customers, no matter what device they were using to access the company’s applications.

To most effectively collect the data, I created an algorithm that gathered all customer feedback and organized it based on certain keywords included in customer entries. I managed to streamline the process of gathering and analyzing these large amounts of data, making it easier to group the information and draw relevant conclusions from it.”

Read more: How to Use the STAR Interview Response Technique

3. How do you overcome any professional challenges?

This question allows you to showcase your problem-solving and critical thinking skills in the workplace and within a team environment. Data scientists often handle complex problems, so your answer should demonstrate your ability to overcome obstacles and remain focused while finding solutions. Select a particular project or moment in which you overcame a challenge by using your skills to illustrate your potential with the company.

Example: “In a team environment like this one, I feel it’s best to have an open discussion with my colleagues to discover ways in which we can overcome an issue. At my previous job, my team was responsible for analyzing a new subset of data for the marketing department. we were given the task of going through a large amount of data but there were no clear guidelines on what each team member was responsible for. I organized a meeting with all team members and our managers to clearly outline everyone’s tasks. As a result, we created an efficient system for delegating tasks when given new projects.”

4. What tools and devices do you plan to use in your role as a data scientist?

The purpose of this question is to determine what programming languages and tools you have experience with. In your answer, you can list the tools you frequently use in addition to describing how you use them to successfully and efficiently complete tasks. Consider discussing a recent project you completed, focusing on a single or set of languages or tools you used to overcome a challenge.

Example: “I recently completed an important research project that provided insight into what product design would be more attractive to customers. I had previous experience with SQL and Tableau but was new to FUSE and Python. For this project, I was responsible for gathering and sorting large amounts of data using the FUSE and Tableau platforms for data mining and drawing references. I then used Python to implement algorithms and SQL to update my database when new data was collected. After three months on the project, I expanded my knowledge and application of SQL and Tableau and become proficient in Python, though I am eager to practice with it more.”

Read more: Common SQL Joins Interview Questions

5. What is selection bias, and why do you need to avoid it?

Questions regarding selection bias are very common in data science interviews because they allow you to demonstrate your ability to select completely random sets of data to ensure insights are effective. Defining selection bias, explaining its importance and mentioning the methods you use to avoid it can showcase both your knowledge and your personal opinion on the subject.

Example: “Selection bias refers to the inability to extract random samples of data. I avoid selection bias in all of my projects because data science relies on the randomness of the selected samples when comparing them to the entire database to ensure the validity of the findings.

In my final project in my undergrad program, I had to organize all professional basketball players in the state according to the projected statistics for the upcoming season. I used boosting, weighting and resampling to make sure I avoided subconsciously being biased toward players the ones that were my favorites. This process ensured my data most accurately reflected the element I was reporting on.”

6. How do you organize big sets of data?

As a data scientist, you will frequently need to merge large sets of data obtained through various platforms, organizing them in a way that allows further analysis. This is an important question because it tests your knowledge and ability in organizing large data. Your answer should show that you are familiar with both the processes and the tools required for organizing data. Consider discussing an experience you have organizing a large set of data, identifying the tools you used and the results of your process.

Example: “In my last position, I organized big sets of data by first determining their relevance and eliminating the data sets that do not comply with the determined logic. I recently had to organize a list of all state residents that have diabetes according to age, gender and other relevant factors. I managed to organize the data by using Paxata to help automate the cleanup process. Determining the relevance of data points and using Paxata helps me collect the most important data and discover the most effective insights.”

Read more: Technical Skills: Definition and Examples

7. Is having large amounts of data always preferable?

This is a question that often comes up in data science interviews and aims to determine the applicant’s philosophy and general thinking when it comes to data. You can provide a balanced answer that discusses how the preferable amount of data typically depends on the context. Use the STAR method to illustrate your knowledge with specific professional experience.

Example: “A cost-benefit analysis is usually required to determine if large amounts of data are preferable. There are costs involved in having a vast amount of data, from computational power to memory requirements. Therefore, determining if the data is unbiased and relevant may be more important than its quantity.

I previously worked for a company that did electoral surveys for local elections. My task was to sort the received data based on the age and occupation of the people inquired. Upon analysis, I discovered that large numbers of citizens had many relevant similarities and concluded that, even though we had gathered data from a large number of subjects, a smaller number of subjects would have delivered similar results.”

8. What is root cause analysis?

Using data to discover and fix various issues is a large part of a data scientist’s job. Root cause analysis is a vital part of that process that tries to find the original fault to determine the sequence of problems that lead to faults in a certain process. Your answer should demonstrate your theoretical knowledge and practical experience conducting a root cause analysis. This question is your opportunity to show your prospective employer that you are well-equipped for this data science position.

Example: “Root cause analysis is a technique that is used to reverse-analyze an issue to determine the original flaw that led to that issue. My previous experience with root cause analysis is when I was working for a manufacturing company and was tasked with using root cause analysis to determine process anomalies like component failures, corrupt sensor values, as well as changes made to the control logic and environmental conditions. I successfully created an algorithm that formed predictions based on current behavior patterns, which lead to significantly fewer flaws in the production process.”

9. How do you usually identify outliers within a data set?

Successful data scientists need to be able to use their theoretical knowledge to produce practical, real-world outcomes and conclusions. This question is your opportunity to showcase your analytical skills and the ways you use them to determine outliers and other data impacts in a variety of contexts. For an effective answer, use a specific professional experience that best illustrates your knowledge.

Example: “Typically, I use practical methods and first analyze the raw data to understand the general trends. I can then determine which model will enable me to detect any outliers. For example, I recently compiled data of all professional basketball players in the state based on their points-per-game average. I managed to successfully identify outliers by creating histograms for each player and used statistical techniques such as quartiles and inner and outer fences to check the accuracy of my findings.”


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