There’s a ton of hype in the data science industry right now, and the general consensus is that it’s currently quite difficult to hire quality data scientists.

Given this tight hiring environment, something that has been confusing my group of industry buddies is how some managers are still apparently looking for ways to alienate nearly their entire data science team.

With that in mind, below are some notes we discussed about the best ways to ensure that your entire data science team quits within one year.  Thankfully, I’m not personally dealing with these issues at my job…but some of my friends aren’t so fortunate.

 

Focus on ‘tightening up the process’ with more status meetings, organization

It can be hard for some data science managers – especially those who have maybe never actually done data science themselves – to fully appreciate how disruptive intermittent meetings can be.

I certainly understand the desire to keep track of people’s work, ‘keeping everybody on the same page,’ and whatever other Agile/Scrum-like phrasing you want to use.  However…the problem with this thinking is that you’re essentially assuming that data science follows a generally known and linear process.

I do think that Scrum boards can be useful when trying to do cutting edge data science; after all you do need at least some rough way of tracking progress.

However…when managers get religious about this and demand tickets for every item, and meticulous grouping/maintaining of all the tickets, in spite of how unknown and dynamic the future process is…that’s a great way to get a data scientist to quit.

At the end of the day, that type of ‘make the creative thought process linear’ perspective certainly has its place – it’s just a completely different way of thinking from how most data scientists think.  And that mental cost of context-switching is huge.

Put another way: if you’re a data scientist manager, are you worshipping the process – or are you more interested in the result?

 

Promoting people who specialize in buzzwords

If you (as a manager) have ever done this before, you almost certainly didn’t think of it this way.

For example, for a hypothetical “Josh”, your thought process might have gone something like:

“Hey Josh over here is well-spoken, carries himself well, has great leadership qualities, and always seems to have a cool tidbit to talk about from the latest analytics conference.  And everybody seems to like him.”

There’s nothing wrong with that – this ‘Josh’ seems like a great guy, and would probably be a valuable manager for many different team types.

 

However…the way this might look to a data scientist:

“Josh seems alright…but has he ever actually delivered something?  He seems to be throwing around lots of impressive words, and speaks quite confidently – but, some of the things he says, half the time I’m not actually sure he has any idea what he’s talking about.”

If someone like ‘Josh’ gets promoted over a data scientist who actually produces – especially with little to no explanation from upper management of why that promotion decision was made – that could make some people quite upset.

 

Hire non-technical management consultants to give ‘feedback’ on technical matters

I have nothing against consultants – I used to be a consultant, and I know there are some truly world-class technical consultants out there.

However, when companies bring in consultants, something my industry group has noticed is that upper management sometimes does a terrible job of communicating with employees about what exactly is going on.

As in, ‘hey there’s going to be some consultants talking to you’ …and that’s the extent of the communication.

One of my buddies was relaying a horror story of how this high-energy, relatively fresh MBA  consultant (with apparently little technical skill and zero data science experience) came in and essentially started grilling my buddy’s data science team about why they weren’t moving faster.

Long story short, that’s a demoralizing and devaluing experience.  This consultant effectively had no idea what they were talking about when it came to data science – yet they had no issue going off on the team about how they needed more ‘change agents’ or a better ‘greenfield vs. blue ocean strategic approach.’

To be far, I don’t remember the exact details of what my buddy was saying – but he was not happy.

 

Conclusion

This isn’t a comprehensive list; these items are just the most recent hot takes that my industry buddies had about some great ways to get your data science team to quit.

Granted, thankfully most of this behavior doesn’t seem that common (at least in our tiny sample set) – but when it does happen, it can have quick ramifications on your data scientist retention efforts.

 

 

The views expressed on this site are my own and do not represent the views of any current or former employer or client.