Data Science Triple Threats

Everyone likes a good list. There are some articles working around which talk about why Data Science doesn’t add business value. As a data scientist who love people, I wanted to add my voice to conversation. Here are three things that make it hard to work with a team of Data Scientists (& Engineers).

  • Data Scientists are less likely to be mobilized on incomplete information.

A data scientist, or perhaps a PhD in general, wants to fully understand the problem before choosing a solution. This is best explained in an example: There may be one business rule which turns a problem from linear to non-linear.  But, the effect of that non-linear portion is small, so the business doesn’t bother to mention it until part way through the build process. The business doesn’t understand that the data scientists might literally need to start over to incorporate that new feature. As a result, data scientists have developed a healthy suspicion of project requests. No one wants to start over because the problem wasn’t accurately described first. So, the team stalls until they believe they understand the full problem. And that can take a long time. It’s a big challenge to begin a problem fast and get quick wins while simultaneously going slowly enough to protect from future disruptions.

  • Data Scientists will not believe something until they see it with their own eyes.

This personality quirk is very important to their job.  It means they question everything, validate unknowns and solve “unsolvable” problems. (After all, if you believe your colleague who says that the problem is unsolvable… then you aren’t going to be the one to solve it!).  However, it’s challenging to have a team that won’t accept second hand knowledge. Teams are forced to include the DS in every meeting, in order to build the requisite business knowledge.  Meanwhile the DS might be pushing the meeting down a tangent which is not the main focus of the meeting.  This, in combination with #1, is a hard problem.

  • Data Scientists require leaders who are Triple Threats.

In the performing arts a Triple Threat is someone who can sing, dance and act. In Data Science, a triple threat is someone who can understand the Mathematics, the Business and the Communication necessary to be a liaison between the first two sets of people. And often these traits are negatively correlated. People who are good at Math are certainly perceived to be less good at people. Thus, Triple Threats are rare!

Incorporating mathematicians into the workplace is more valuable than ever. Finding and acquiring a triple threat can be a challenging prospect, but something which companies should not shy away from.

What can we do about these challenges? Have you made progress on solving any of these challenges? What do you think are the biggest challenges facing data scientists right now?

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About Samantha from SocialMath

Applied Mathematician and writer of socialmathematics.net.
This entry was posted in Business, Communicating Math and tagged , , , , , . Bookmark the permalink.

One Response to Data Science Triple Threats

  1. A big challenge facing data scientists is bias. Sometimes people really want a hypothesis to prove true. Instead of following the data where it will leads, a data scientist will conveniently find all the data that will support the assertions being made. Of course this is not isolated to data science, but rather it is a problem that people have in general.

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