Some data scientist personality traits

With all the hype currently surrounding the data science industry, we’re increasingly seeing people from a wider range of backgrounds thinking about whether they should themselves become a data scientist.

A challenge for many new potential entrants is that they haven’t really considered whether they’re comfortable with the mindset required to be at least a somewhat-successful data scientist.

Below are my thoughts on personality traits that you’ll generally see from high-performing data scientists.  It’s neither an exhaustive nor authoritative list; just something that me and my little industry group generally agree upon:


Relentlessly curious

If you were to take a random sample of ten high-performing data scientists, you’d probably find that at least seven of them strike you as notably curious.

You kind of have to be curious to be a data scientist.  Nearly your entire job is to find insights in the data that probably no one else has ever found, and then make a credible and engaging presentation of why the insights actually matter.

If you don’t really care about fundamentally learning more from your data set, you’re probably not going to have an especially fulfilling data science career.


Humility + never stops learning

Like probably many data scientists, I have a lot of respect for the late physicist Richard Feynman.

There’s a lot of things to like about him, but something I deeply appreciated about him was his innate humility and philosophy on learning.  Specifically, always acknowledging that, pretty much no matter what you know, there’s almost certainly a ton more that you don’t know:

The first principle is that you must not fool yourself and you are the easiest person to fool.

-Richard Feynman

When you think you have all the answers, you’ll stop looking for new knowledge; you’ll stop caring about learning.  And looking for new knowledge…is kind of the whole point of being a data scientist.


Openness to new ideas 

I’m a pretty big believer that the data science industry is rapidly going through some rapid, fundamental changes.

Even if you were superhuman and were somehow able to know everything there is to know about data science right now…a good part of your knowledge will probably become functionally obsolete within three years.  Or at least require major modification.

Something I struggle with is getting comfortable with the current technology and tools I use on a daily basis – and not unwittingly becoming that old curmudgeon who puts up a lot of resistance when new tools are (justifiably) suggested.  It’s something I’m working on; I’m currently trying to get myself to transition from R to Python.

However, I think an even bigger issue is when we sometimes start thinking that we ourselves have become ‘the’ expert within a certain subfield within data science.  That is the day we stop learning, stop listening to new ideas, and stop searching for new knowledge.  That’s also how you alienate an entire generation of younger data scientists.

To the person who thinks they’re king, new ideas are nothing more than a challenge to their ‘authority’ – and not even worth really considering.  More of a nuisance to wave away.

This isn’t only a purely theoretical concern; it’s an increasingly discussed problem within the scientific and research community.  People start thinking they have all the answers, and not only stunt their own personal growth, but actively stunt the growth of others.


Not afraid of asking ‘dumb’ questions

The more you’re hanging out on the cutting edge, the less likely any question you ask will actually be ‘dumb.’

Put another way, if you’re not asking a ton of questions, you’re probably either (a) not trying or (b) not really hanging out at the cutting edge.

I was talking to someone from my little data science industry group about this, and one thing we agreed on is how you’ll almost never see an experienced, high-performing data scientist berate someone for asking a ‘dumb’ question.

The philosophical message you send when you start being the dumb-question police: we hate learning, everyone here is perfect, reputation is way more important than learning, get back in line, we embrace mediocrity.

If you show me a data scientist who has stopped asking questions, I’ll show you a data scientist who is about to exit the industry.


Wrapping up

There’s a ton of ways to become successful in data science, and I don’t at all claim I know the path to get there.  Quite the opposite – I know very little, but at least am actively trying to improve.

However, if you’re looking to get into data science, and none of the above personality traits describe you…then you might want to consider asking around a bit more before committing to becoming a data scientist.



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

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