Data Scientist (Analyst) Interview Questions

Planning to bring a Data Scientist on board? Before going on the hunt for a new employee, take a look at these Data Scientist (Analyst) interview questions.

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Data Scientist (Analyst) Interview Questions

Businesses across different industry sectors have come to realize the benefits Big Data can offer, and hence are looking forward to capitalizing on the same but in order to leverage big data a business must be armed with necessary know-how, tools, and manpower to mine information and make an accurate analysis. This requires specialized skills of a Data Scientist.

The requirements for Data Science and Analytics jobs are often multidisciplinary. Candidates with a strong Mathematics or Science background with rigorous Statistical training can be ideal for this role. Also, look for exceptional ability to link analytics to creating value for the company.  Successful Data Scientists stand out at deriving meaningful insights from the data that company creates.

Here is a list of Data Scientist (Analyst) interview questions that will surely lend a helping hand in recruiting the most competent aspirant.

Operational questions

Data Analytics Interview Questions

  • Which models would you characterize as complex models, and which ones as simple? Mention the relative strengths and weaknesses of picking a more complex model over a simpler one.
  • What are the different steps that you follow to pre-process the data before using it to train a model and under what conditions these steps can be applied?
  • How do you predict categorical responses?
  • Explain dimensional reduction and state the best ways to perform this.
  • What is the use of Combinatorics in data science?
  • What is regularization and how is it useful?
  • What do you understand by price elasticity, price optimization, competitive intelligence, and inventory management? Explain with examples.

Job-specific questions

Statistics

  • Define confidence interval and its benefits.
  • State the difference between correlation and statistical independence.
  • What is the difference between univariate, bivariate and multivariate analysis?
  • What do you mean by recall and precision? How are they related to the ROC curve?
  • Explain resampling methods along with their benefits and limitations.
  • What is selection bias and how do you avoid it?
  • What assumptions are required for linear regression?

Programming

  • Which programming language and environment are you most comfortable working with?
  • Have you created any original algorithm?
  • Explain how will you clean a dataset.
  • Name your favorite statistical software. Also, mention the pros and cons of using the software.
  • What is the easiest way to sort a large list of numbers?

Modeling

  • Name the data visualization techniques that you prefer the most.
  • What is the best way to effectively represent data with 5 dimensions?
  • Are you familiar with time-series model? What is your understanding of cross-correlations with time lags?
  • What is the 80/20 rule? How is it important in model validation?