Data Scientist Interview Questions

Interview preparation is a key step in starting your career as a Data Scientist. Mock interviews and reviewing potential interview questions can help you ace job interviews. Practice helps you demonstrate your confidence and capability to recruiters. The following sections detail what to expect in a Data Scientist interview, top questions for Data Scientists, and how to respond.

What to Expect in a Data Scientist Interview

Interviews for Data Scientist positions typically take multiple rounds to complete. They also commonly include a testing component. Here is how most Data Scientist job interviews will progress: 

1. Online Test/Screening: To help filter out unqualified candidates and to prove competency, many data science positions may follow up your resume submission with an online test. For Data Scientists, this online screening may test your knowledge of Python programming, analytics, computer science, and other data science skills. This test may last between one and two hours.

• Phone Interview: Once a recruiter has reviewed your resume, they will set a time for a phone interview lasting around 15 to 30 minutes. This interview is usually short and aimed at gathering additional information, such as why you are interested in the role, your salary expectations, and your potential start date. 

• Technical Interview: During the technical interview stage, you may be asked to complete a data science assignment within one or two weeks. This assignment will demonstrate to the employer your skills, knowledge, work style, and ability to follow instructions. 

• HR Interview: Following the technical interview, HR may arrange for another interview with you to ensure your interest and compatibility with the role, team, and organization. 

• Leadership and Team Interview: Finally, you will meet with members of the data science team including potential coworkers, managers, and stakeholders. If you are interviewing for an in-office position, this interview will typically take place in person. This is a chance to meet you face-to-face, have the team ask you questions, and gauge how you interact with people you will likely be working with every day. 

Top Interview Questions for Data Scientists

Even the most qualified candidates still get nervous when it comes to interviewing. It’s natural to feel some pressure and anxiety when a recruiter or an entire team of people have their attention focused solely on you. That’s why thoroughly preparing for interviews helps you to answer questions confidently so you can demonstrate your capabilities. Not every interview question has a “correct” answer, as some will be used simply to gather information about your work style, but the more questions you anticipate, the more your professionalism comes through.

The sections below detail common interview questions asked of Data Scientists and offer suggestions for how to respond.

How would you explain linear regression?

An interviewer might ask you to generally speak about your knowledge and skills regarding linear regression or may ask a more specific question such as “What are some drawbacks to using a linear regression model?” Reviewing your knowledge of linear regression best practices can help you prepare for questions like this.

How to answer

Linear regression helps to define the relationship between an independent and dependent variable. By understanding this relationship, one can predict future relationships between input (X) and output (Y). Simple linear regression concerns just one independent variable. A regression involving multiple independent variables is called multiple linear regression.

How would you explain logistic regression?

The interviewer may ask this or another open-ended question that permits you to demonstrate your understanding of the topic. Reviewing best practices and how to define logistic regression in data science will help you prepare for this and similar questions.

How to answer

Logistic regression predicts a binary outcome based on analysis of prior observations in a dataset. By observing the relationship between one or multiple existing independent variables, this statistical analysis method can predict outcomes with only two possibilities, such as “yes” or “no.”

What is a confusion matrix?

The interview may ask this and related questions to test your understanding of a confusion matrix and how it applies to machine learning. This and similar questions are designed to test your knowledge and understanding of data modeling techniques.

How to answer

In terms of machine learning, a confusion matrix (sometimes called an error matrix) is a table used to visualize and estimate an algorithm’s performance. A confusion matrix tabulates the predicted and actual values in a 2x2 matrix.

What is the difference between supervised and unsupervised learning?

This sort of question will test your understanding of machine learning techniques. Both supervised and unsupervised learning techniques allow Data Scientists to build models. However, they have different use cases. 

How to answer

Supervised learning applies to data containing inputs and the expected output. Data Scientists use supervised learning to build models that can predict and classify data. Supervised learning algorithms include decision trees and linear regression. 

Unsupervised learning works on unlabeled data. Data Scientists use unsupervised learning to extract valuable information from large data sets. Unsupervised learning models include the Apriori algorithm and K-means clustering.

How often should algorithms be tested and updated?

An interviewer may ask this question as an open-ended question or about specific algorithms, especially those related to the organization. 

How to answer

Researching the organization before your interview and looking for information on how they use data science will help you not only answer this question, but relate your answer directly to the work of the organization. This will demonstrate your understanding of data science and your understanding of the organization and its goals. This provides an opportunity to show that you know how to apply your knowledge and skills to this specific position.

Explain this portion of your resume.

During a Data Scientist interview, you may be asked to elaborate on skills and experiences you highlight in your resume. 

How to answer

Reviewing your resume prior to your interview will help you prepare answers. Before even submitting a job application, it pays to proof your resume and review it with a mentor when possible. This will help to ensure the professionalism of your resume and serve as an opportunity to tailor your resume to the position you are applying for.

Explain this aspect of your professional portfolio.

An interviewer may ask you to explain a project from your portfolio. This provides a chance for you to elaborate on the skills and knowledge you contributed to this project. This also allows you the chance to show that you know how to effectively communicate your findings and explain data science reports to stakeholders, which is a key component of many Data Scientist jobs.

How to answer

Refresh your memory of your portfolio projects prior to the interview. Make sure you know how to articulate the purpose of the assignment, your findings, your technique, and your specific contributions to the assignment. Reviewing your portfolio with a mentor will help you to prepare accordingly and to ensure your portfolio contains work that demonstrates your capabilities and relates to the position you are applying for.

Learn the Skills to Become a Data Scientist at Noble Desktop

If you are looking to start a new career in data science, you might think the only way for you to become a Data Scientist is by enrolling in a four-year university or pursuing other costly and lengthy educational options. However, there are many alternative methods available to help you transition into a data science career, including data science bootcamps and certificate courses designed to help working professionals gain the skills needed to obtain an entry-level job as a Data Scientist. Exploring in-person and live online data science bootcamps and certificate programs can help you find the class that meets your career goals, budget, and schedule. The first step to finding the class that fits your needs is to understand the differences between in-person classes and live online classes. 

In-person data science classes meet in a traditional classroom setting at a physical location. In-person classes have the advantage of providing all necessary equipment, such as computers and software, and allowing students to network with local professionals such as your classmates and instructor. You also have the advantage of learning from an expert instructor face-to-face. The primary drawback to in-person courses is the extra time and money required to commute to the physical learning location. Live online data science classes offer many of the same benefits as in-person classes, including the ability to learn in real-time from an expert instructor. You can also collaborate with classmates, and you have the advantage of learning remotely. 

Noble Desktop offers several different in-person and live online data science classes that can help you start a career as a Data Scientist. The Python for Data Science Bootcamp teaches students foundational programming concepts and how to handle different data types, use conditional statements to control the flow of a program, use Scikit-Learn, Matplotlib, Numpy, Pandas, and other Python libraries and tools. Noble’s Data Science Certificate program and Data Analytics Certificate program provide a deep dive into the topics and skills essential to launching a career in data science or data analytics and offer one-on-one mentorship and job search assistance. All Noble Desktop classes provide students with hands-on experience, flexible financing options, setup assistance, a free retake, small class sizes, and real-time guidance from an expert instructor.

Learn more about Noble Desktop’s in-person and live online data science classes.

You can also learn more about data science careers and data science learning options with Noble’s free Data Science Learning Hub.