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How to Become a Data Scientist

Data Science Interview Questions

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Data science interview processes can vary depending on the company and industry. Typically, they will include an initial phone screening with the hiring manager followed by one or several onsite interviews. You will have to answer technical and behavioral data science interview questions and will likely complete a skills-related project. Before every interview, you should review your resume and portfolio, as well as prepare for potential interview questions. Data science interview questions will test your statistics, programming, mathematics, and data modeling knowledge and skills. Employers will be assessing your technical and soft skills and how well you would fit in with their company. By practicing some common data science interview questions, you can enter the interview with confidence. There are a few different types of Data Scientist questions that you can expect to encounter during your data science interview.

Employers are looking for candidates who have a strong knowledge of data science techniques and concepts. Data-related interview questions will vary depending on the position and skills required. Here are some examples of data-related interview questions:

  • What is the difference between supervised and unsupervised machine learning?
  • Explain Decision Tree algorithm in detail.
  • What is the difference between machine learning and deep learning?
  • What is sampling? How many sampling methods do you know?
  • What is the difference between type I vs type II error?
  • What is linear regression? What do the terms p-value, coefficient, and * r-squared value mean? What is the significance of each of these components?
  • What is a statistical interaction?
  • What is selection bias?
  • What is an example of a data set with a non-Gaussian distribution?
  • What is the Binomial Probability Formula?
  • How is k-NN different from k-means clustering?
  • How would you create a logistic regression model?
  • Explain the 80/20 rule, and tell me about its importance in model validation.
  • Explain what precision and recall are. How do they relate to the ROC curve?
  • Explain the difference between L1 and L2 regularization methods.
  • What is root cause analysis?
  • What are hash table collisions?
  • What are some of the steps for data wrangling and data cleaning before applying machine learning algorithms?
  • What is the difference between a box plot and a histogram?
  • What is cross-validation?
  • Explain what a false positive and a false negative are. Is it better to have too many false positives or too many false negatives?
  • In your opinion, which is more important when designing a machine learning model: model performance or model accuracy?
  • What are some situations where a general linear model fails?
  • Do you think 50 small decision trees are better than a large one? Why?

List of Data Science Interviews Questions: Technical Skills Questions

Technical skills questions are used to assess your data science knowledge, skills, and abilities. These questions will be related to the specific job responsibilities of the Data Science position. Technical skills questions may have one correct answer or several possible solutions. You will want to show your thought process when solving problems and clearly explain how you arrived at an answer. Examples of technical data science skill interview questions include:

  • Tell me about an original algorithm you created.
  • What are some pros and cons of your favorite statistical software?
  • Describe a data science project in which you worked with a substantial programming component. What did you learn from that experience?
  • How would you effectively represent data with five dimensions?
  • Assume you need to generate a predictive model using multiple regression. Explain how you intend to validate this model.
  • When modifying an algorithm, how do you know that your changes are an improvement over not doing anything?
  • What is one way that you would handle an imbalanced data set that’s being used for prediction (i.e., vastly more negative classes than positive classes)?
  • How would you validate a model you created to generate a predictive model of a quantitative outcome variable using multiple regression?
  • I have two models of comparable accuracy and computational performance. Which one should I choose for production and why?
  • You are given a data set consisting of variables with more than 30 percent missing values. How will you deal with them?

List of Data Science Interview Questions: Personal Questions

Along with testing your data science knowledge and skills, employers will likely also ask general questions to get to know you better. These questions will help them understand your work style, personality, and how you might fit into their company culture. Personal Data Scientist interview questions may include:

  • Tell me about yourself.
  • What are some of your strengths and weaknesses?
  • Which Data Scientist do you admire most?
  • What do you think makes a good data scientist?
  • How did you become interested in data science?
  • What unique skills do you think you can bring to the team?
  • Why did you leave your last job?
  • What kind of compensation are you looking for?
  • Give a few examples of best practices in data science.
  • What’s a data science project you would want to work on at our company?
  • Do you work better alone or as part of a team of Data Scientists?
  • Where do you see yourself in five years?
  • How do you handle stressful situations?
  • What motivates you?
  • How do you evaluate success?
  • What type of work environment do you prefer?
  • What are you passionate about outside of data science?

List of Data Science Interview Questions: Leadership and Communication

Leadership and communication are two valuable skills for Data Scientists. Employers value job candidates who can show initiative, share their expertise with team members, and communicate data science objectives and strategies. Here are some examples of leadership and communication data science interview questions:

  • Can you tell me about a time when you demonstrated leadership capabilities on the job?
  • How do you go about resolving conflict?
  • How do you prefer to build rapport with others?
  • Talk about a successful presentation you gave and why you think it went well.
  • How would you explain a complicated technical problem to a colleague/client with less technical understanding?
  • Describe a time when you had to be careful talking about sensitive information. How did you do it?
  • Rate your communication skills on a scale of 1 to 10. Give examples of experiences that demonstrate the rating is accurate.

List of Data Science Interview Questions: Behavioral

With behavioral interview questions, employers are looking for specific situations that showcase certain skills. The interviewer wants to understand how you dealt with situations in the past, what you learned, and what you are able to bring to their company. Examples of behavioral questions include:

  • Tell me about a data project you have worked on where you encountered a challenging problem. How did you respond?
  • Have you gone above and beyond the call of duty? If so, how?
  • Tell me about a time when you had to clean and organize a big data set.
  • Tell me about a time you failed and what you have learned from it.
  • How have you used data to elevate the experience of a customer or stakeholder?
  • Provide an example of a goal you reached and tell me how you achieved it.
  • Provide an example of a goal you did not meet and how you handled it.
  • How did you handle meeting a tight deadline?
  • Tell me about a time when you resolved a conflict.

List of Data Science Interview Questions From Top Companies (Amazon, Google, Facebook, Microsoft)

To give you an idea of some other questions that may come up in an interview, we compiled a list of data science interview questions from some of the top tech companies.

  • What’s the difference between logistic regression and support vector machines? What's an example of a situation where you would use one over the other?
  • What is the interpretation of an ROC area under the curve as an integral?
  • A disc is spinning on a spindle and you don’t know the direction in which way the disc is spinning. You are provided with a set of pins. How will you use the pins to describe in which way the disc is spinning?
  • What would you do if removing missing values from a dataset causes bias?
  • What kind of metrics would you want to consider when solving questions around a product’s health, growth, or engagement?
  • What metrics would you assess when trying to solve business problems related to our product?
  • How would you tell if a product is performing well or not?
  • How do you detect if a new observation is an outlier? What is a bias-variance trade-off?
  • Discuss how to randomly select a sample from a product user population.
  • Explain the steps for data wrangling and cleaning before applying machine learning algorithms.
  • How would you deal with unbalanced binary classification?
  • What is the difference between good and bad data visualization?
  • How do you find percentiles? Write the code for it.
  • Create a function that checks if a word is a palindrome.
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