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16 Reasons Data Scientists are Difficult to Manage - I found this sad and upsetting

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16 Reasons Data Scientists are Difficult to ManagePosted by  Amy  on November 2, 2013 at 2:30pmSend Message     View Blog

Data scientists

  • are creative and bring disruptive IP (intellectual property), and this can cause havoc for their company. They can steal and leverage your IP, and create IP leaks.
  • are not great communicators, and sometimes can be stubborn
  • 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
  • hate doing mundane work, and are bad at it
  • have more career options than many employees, and are thus difficult to retain
  • don't like team work, tend to be elitist and isolationist
  • 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)
  • are not good listeners
  • work for you just to save enough money to launch their company and compete with you in a couple of years
  • are arrogant: bad impact on teams
  • think sales, marketing and executives are stupid
  • can do real nasty stuff if they become a disgruntled employee
  • are sometimes reluctant to share their knowledge, train colleagues, or outsource to colleagues
  • are not great at prioritizing
  • are not great at switching (on-demand) from one task (coding) to another (presenting)
  • 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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I found this post pretty sad... really spoke more about the managers of the data scientists and probably a fundamental lack of understanding of how data and business are related in today's organization. 

Here is my reply:

Wow, that's a pretty acidic list - here's a thought for you:

If you walk down the street scowling, then people will generally avoid looking at you or scowl back. However, when you walk down the street smiling ... different result.

That hugely negative list in my opinion is a result of very poor management and the behaviors and attitudes listed are a direct result. When I read that list, it feels like something an 18th Century Slave owner might write of their slaves. Or a 19th Century industrialist might claim of their factory workers. Quite sad that these management opinions are still held

I have managed data analysts, data scientists and research scientists - in addition to product managers and a number of other individuals in other business and technical roles. I fundamentally believe that people want to do a good job. As a manager, my job was to find out what people are good at, what motivates them and then to construct a role and working environment where they thrive. 

I will pluck out a couple of the examples here:

  1. are creative and bring disruptive IP (intellectual property), and this can cause havoc for their company. They can steal and leverage your IP, and create IP leaks. - innovation is disruptive by its very nature, and data scientists should be placed in a department where innovation and breaking current models and modes of thinking is the objective of their work. The IP should be protected legally through their contracts of employment and this made explicit. Also their work should be the subject of ongoing conversation within the group - the manager should be engaging them in debate as to what they are discovering. 
  2. are not great communicators, and sometimes can be stubborn - some can be poor communicators, in which case, they may not be necessarily good data scientists in a professional role (a person may be the most amazing and talented cook but if they are unable to cook on a line or manage a kitchen, they are not necessarily a good chef). Same with the stubbornness. Perhaps the hiring manager is at fault for simply hiring someone with the right academic qualifications. However, there are ways of managing introverts too. 

That's just two out of that list, but for me, I feel that the list is actually a set of opportunities for better management of a function that is very poorly understood. 

[If you are a data scientist that is feeling unloved, come see us at, happy to talk]

Well said. 

The original article was filled with flawed logic. 

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