Data Scientist (Analyst)

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?