Big Data Word Cloud

We recently wrote a post about a report which revealed some of the most in-demand skills in jobs at the moment. As well as hot skills, there were some key tech roles on the rise: cyber security engineers, mobile application developers and network architects to name a few.

Reports also suggest demand for data scientists is on the rise, we’ve compiled a list of 20 interview questions data scientists should expect to be asked at interview.   1. How did you become interested in data science? 2. What types of people are you used to working with? Internally/Externally/C-level? 3. What was the most recent conference/webinar/class/workshop/training you attended? 4. What is the most recent programming skill that you acquired? 5. Please give a few examples of ‘best practices’ in data science 6. Do you have any experience using API’s? 7. What do you think makes a good data scientist? 8. What is your favorite programming language/vendor? 9. What is the biggest data set that you processed and how did you process it – what were the results? 10. Describe the biggest problem you have dealt with in data analysis? 11. How do you handle missing data? If this were to happen what imputation techniques would you recommend? 12. Have you been involved in database design and data modeling? 13. How would you turn unstructured data into structured data? 14. When is it better to write your own code than using a data science software package? 15. Which tools do you use for visualization? Do you use Tableau/R/others? 16. Are you familiar with software life cycle? 17. What is an efficiency curve? What are its drawbacks and how can this be overcome? 18. What is a recommendation engine? How does it work? 19. What is an exact test? How and when can simulations help us when we don’t use an exact test? 20. What could make a chart misleading, difficult to read or interpret? What features should a useful chart have?  

Would you add another one to the list? Tweet us @K2Partnering / @K2Careers using #k2interviewtips we’d love to hear from you.




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