Of course each data scientist is different, so please take this criticism with a grain of salt. By a long stretch, they don't apply to all data scientists.

Reasons:

1. Data scientists are creative and bring disruptive IP (intellectual property), and this can cause havoc for their company. 
2. They can steal and leverage your IP, and create IP leaks.
3. Are not great communicators, and sometimes can be stubborn
4. Work on stuff that they like, even if it does not translate in yield, or if it is not stuff they are paid to do
5. Hate doing mundane work, and are bad at it
6. Have more career options than many employees, and are thus difficult to retain
7. Don't like team work,
8. Tend to be elitist and isolationist
9. Are sometimes very attached to a specific technology and don't want to try something different (they sometimes give the impression that they haven't realized the world is evolving without them)
10. Are not good listeners
11. Work for you just to save enough money to launch their company and compete with you in a couple of years
12. Are arrogant: bad impact on teams
13. Think sales, marketing and executives are stupid
14. Can do real nasty stuff if they become a disgruntled employee
15. Are sometimes reluctant to share their knowledge, train colleagues, or outsource to colleagues
16. Are not great at prioritizing
17. Are not great at switching (on-demand) from one task (coding) to another (presenting)
18. Sometimes have issues working with women (especially managing or being managed by women), especially if coming from a male-dominant culture

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Additional Reading

• 50 Articles about Hadoop and Related Topics
• 10 Modern Statistical Concepts Discovered by Data Scientists
• Top data science keywords on DSC
• 4 easy steps to becoming a data scientist
• 13 New Trends in Big Data and Data Science
• 22 tips for better data science
• Data Science Compared to 16 Analytic Disciplines
• How to detect spurious correlations, and how to find the real ones
• 17 short tutorials all data scientists should read (and practice)
• 10 types of data scientists
• 66 job interview questions for data scientists
• High versus low-level data science

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