One of the hottest careers today involves the coupling of statistics and computer science. Big data is everywhere. Companies are realizing that the power and knowledge involved with making smart business decisions, often lie not only with those who have the best data access, but also the deepest understanding of that information. Welcome to the age of data science and the rise of the data scientist.
A recent study predicts that by 2018, there will be a shortage of as many as 1.5 million analysts with the technical know-how to use big data effectively. But technical skills won’t be the only thing employers will be looking for; people who are naturally curious with the flexibility and creativity to think beyond traditional ways of using information to solve problems will be in demand. So, expect questions that focus as much on the art of data science as the science.
Sample of Possible Interview Questions for Data Scientists:
You are about to send an email for a marketing campaign. How do you optimize delivery and response?
What are the assumptions required for linear regression?
What is an efficiency curve?
How do you find the median of a large dataset?
If we were testing a product, what metrics would you look at to determine if it is a success?
Discuss a data scientist being a combination of both a scientist and an artist.
How do you determine how much information to present to non-technical people in your company to help them make the best business decisions?
What do you do when someone challenges your analysis?
Is there something you have had an interest in researching on your own, outside of work assignments?
What kinds of projects are you most engaged in?
What data would you analyze when hiring a data scientist?
Describe the most efficient way to ensure everyone at the company has access to the analytics they need to do their jobs.
What is the most compelling data set you have worked on?
Have you ever searched for open data and performed an analysis on it?
What are your favorite open-source tools?
Describe a situation where your intuition changed how you made a decision.
Discuss the following: “All models are wrong. Some are useful.”
Do you create a benchmark of your work for the future?
How do you know if you have asked the right questions about what you are looking for prior to analysis?