Data Scientist (Analyst)

Find your next star Data Scientist with these sample interview questions. Don’t forget to add questions specific to your company’s position requirements.

Data Scientist qualifications to look for

Top Data Scientists, Managers and Analysts candidates excel at gaining actionable insights from organization-generated data. They have a sixth sense about the right data to collect, and a solid process for carrying out effective data analyses and building predictive models.

Candidates need a strong foundation in statistics, operations research and machine learning as well as database skills such as Python and SQL. This helps them retrieve, clean and process data from a variety of sources. 

Your best candidates will have a background and degree in mathematics or statistics, engineering, or computer science.  

A typical Data Scientist will program in a scripting language such as R, Python or MATLAB, and are able to present the findings of analysis. 

Keep an eye out for candidates who are:

  • Information-visualizers
  • Have knowledge of Tableau or D3.js (or related programs)
  • Strong communicators 

Remember to modify some questions to more quantitative, statistical analysis interview questions. 

Top tip: Hire candidates willing to grow by making sure their personal career goals align with your company's mission.

Problem-solving interview questions

  • Walk me through your step-by-step process to design a data-driven model that solves a business problem. For example, an automated process to segment customer support questions, predict hiring patterns or reduce churn rates. 
  • What are the pre-processing steps carried out on data prior to training a model and state under what conditions they might be applied?
  • Describe the difference between a simple and complex model. Give me a few examples. 
  • How would you combine models to form model ensembles? When would this be advantageous?  
  • Explain dimensionality reduction and ways to perform it. 
  • In what situation would you choose a more complex model over a simpler one? When would this not be to your advantage?

Role-specific interview questions

  • In which environment(s) do you usually run your analyses?
  • Are you familiar with SQL? When have you used it? 
  • What visualization tools have you used? What are your favorite features? 
  • We’d love to see any presentation you’ve prepared. 
  • Describe your experience presenting reports and findings directly to senior management. 
  • How do you feel about public speaking? Have you presented a technical topic to an audience before? If so, how do you explain things to a non-technical audience?
  • What is your measure to know if you’ve collected enough data to train a model?
  • What is the reason for training, test and validation data sets? How they are used effectively?
  • Explain a confidence interval and in what circumstance you would use it.
  • Explain the difference between statistical independence and correlation. 
  • Define conditional probability and  Bayes’ Theorem. When would you put this practice to use?  
  • We are training a model using stochastic gradient descent. How do we know if we are converging to a solution? If a training procedure converges will it always result in the best possible solution?
  • Explain clustering and describe an algorithm that performs it. What measure do you use obtained decent clusters? How do you estimate a good number of clusters to use with our data?
  • Explain why correlation does not imply causation. 
  • Describe the key differences between unsupervised and supervised learning.
  • Describe the key differences between regression and classification.
  • What is a bias-variance tradeoff in statistical models?
  • Explain over-fitting and how it relates to the bias-variance trade-off.
  • Define regularization and give examples of regularization in models.
  • We are training a binary classifier and one class is very rare. What example describes this problem? How should we train this model? What performance metrics should we use?
  • How many unique subsets of n different objects can we make?
  • Explain how to build a data-driven recommender system. Are there limitations to this approach?

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