50 Data Science Interview Questions (With Example Answers)
By Indeed Editorial Team
Published 16 August 2021
The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.
In a data science interview, the interviewer is likely to ask technical questions and questions that help them get to know you. Because of this, it is important to prepare in advance for both types of questions. Reviewing example questions can help you make a positive impression and increase your chance of succeeding in the interview. In this article, we discuss the top 50 data science interview questions, along with tips and example answers to help you prepare.
What are some common data science interview questions?
Common questions to expect when interviewing for a data science position can cover a range of topics. At the beginning of the interview, you may expect general questions that help employers get to know you and how the work culture suits your personality. Employers are also likely to assess your background and educational qualifications to ensure your credentials meet the job requirements.
You can also expect technical questions that test your abilities in programming, machine learning and statistical analysis. Your soft skills are important, too, so it is important to prepare for questions about your leadership, communication, teamwork and interpersonal skills.
5 example data scientist interview questions and answers
It can be beneficial to review sample questions and answers to better prepare for your interview. Here are five examples of common data scientist interview questions and their sample answers:
1. What do you know about this position?
Interviewers often ask this question to assess candidates' initiatives to research the role, organisation and job requirements prior to interviewing. Use your answer to show the interviewer you are enthusiastic about the position and about the company and its mission.
Example: "From my research, I understand your organisation's development team is working on a soft AI application that can handle the sorting and organisation of your clients' data. In my past role, I performed similar tasks to create a storage system through supervised learning to help my employer better manage software development processes."
2. How would your past employers describe you?
This question can help the interviewer evaluate past reviews or feedback when you share what previous supervisors have communicated with you. Use examples that highlight your performance and technical aptitude in data science.
Example: "My previous employer gave me feedback the week before I relocated here, and she also included a letter of reference to describe my experience with the company. She describes me as having strong attention to detail and the ability to build supportive connections with teammates that lead to higher team success. I feel that these qualities can help me contribute to the data scientist role here."
3. What are your favourite tools to use when analysing data?
The purpose of this question is to assess your experience with using various tools and programming when completing data analysis projects. In your answer, give some examples of the programming languages, algorithms, frameworks and analysis tools you favour when completing projects.
Example: "I favour working in SQL and have completed many projects using SQL for relational database management systems. I recently developed a deeper understanding of Tableau, which I have used twice to complete projects that required graphic visualisation of data."
4. What are your qualifications as a data scientist?
You can expect this question during your interview, as it allows employers to understand how well your specific credentials, skills and experience fulfil the job requirements. In your answer, discuss any degrees, certifications and specialised skill sets you have and how these can help you succeed in the position.
Example: "I received my bachelor's degree in data analytics, and I will be receiving my Cloudera certified associate certificate in several weeks. I plan to obtain my CAP certificate, as well, when I build more experience in my career. I enjoy working with statistics, big data and mathematical calculations, and I am excited to contribute to your organisation's success."
5. What do you do to avoid selection bias?
The interviewer is likely to ask about selection bias, as this question can help them assess how efficiently you select random data sets to ensure the most effective insights. Use your answer to demonstrate your ability to select methods that keep samples random and allow you to avoid selection bias.
Example: “I avoid selection bias by ensuring the random selection of sample sets with respect to the data, rather than the variables. This makes each sample set I select from a larger population result in more variables that are representative of the population, reducing the risk of selection bias when gaining statistical insights.”
How do I prepare for a data science interview?
Use the following approaches to get ready for your data science interview:
Review the job requirements. This can help you highlight the qualifications you have that fulfil these criteria.
Research the company. Learning about the role shows the interviewer your initiative and enthusiasm for working at their company.
Review math and statistics puzzles. While not all interviewers ask puzzle questions, these types of questions can help them assess your analytical and logical reasoning, so it is important to prepare in advance.
Are data science interviews difficult?
In general, data scientist interviews can be challenging with respect to the technical questions you might expect. This is why it is important to prepare in advance by first researching the company to understand how it uses data analysis and statistics in its daily operations. Understanding what the company expects of the data scientist position can help you get an idea of the types of questions to anticipate.
Related: How To Prepare for a Job Interview
45 additional interview questions for data scientists
Besides the previous example questions, you may also expect the interviewer to ask additional questions regarding your career interests, background in data science and technology capabilities. Consider these additional interview questions for data scientists:
What aspects of data analysis do you find most challenging? How do you overcome these challenges in your work?
Describe your experience in leading technical teams.
How do you ensure you are aware of recent developments in data management?
How do you determine the type of data with which you work?
How do you organise extensive sets of data?
How do you identify whether there is a correlation or a cause in a data set?
What programs are you proficient with for data management and analysis?
If our finance team wanted to create a five-year forecast, would you apply linear or logistic regression? Why?
Describe your experience integrating data for deep learning.
What machine learning frameworks are you familiar with using?
How have you implemented A/B testing in past data projects?
Which programming language do you feel is most suitable for text analytics?
What is one project you worked on that required cluster sampling? What was the outcome?
Share an example of an iteration from one of your successful data projects. What made it successful?
What parameters do you establish when building an artificial neural network?
How do you ensure the confidentiality and integrity of critical data?
Have you created your own algorithms before? How did you develop them?
How would you approach data cleaning in Python?
When working with large data sets, how do you account for outliers, missing values or transformations?
What are some sorting algorithms you have used in the R language?
If our data team uses Hadoop, how would you integrate it with R for enhanced data analysis?
Describe your proficiency in SQL.
What are some successful projects you have completed in SQL? What made them successful?
What applications would you use recommender systems with?
How do you apply univariate analysis?
What process do you use to define the number of cluster values within a clustering algorithm?
Explain your understanding of auto-encoders.
How would you apply the batch normalisation process to organise and analyse data systems?
What machine learning library do you feel is most beneficial for supervised learning projects?
What steps do you take before you apply machine learning algorithms?
How would you resolve unbalanced binary classifications?
What are the benefits of box plots when visualising big data?
Describe the regularisation methods you can apply when implementing training data?
How do you select metrics for cross-validation?
How would you evaluate a predictive model from multiple regression analysis?
When would you use random forests over a support vector machine?
How do you apply dimension reduction to data?
What types of problems would require the use of principal component analysis?
What is an alternative you would use instead of mean square error?
How do you ensure that the regression models you develop fit with the data?
How would you support finance teams in creating decision trees?
What probability fundamentals are crucial to incorporate in data analysis?
What is the relevance of the Markov chain in fixed data locations?
How can an experimental design help support investigation into behaviour?
When would you apply mean imputation in your analysis?
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