What most students don’t get about the data science industry

With how fast the data science industry is changing, it’s hard (especially for me) to keep up with all that’s going on.

For students, it’s probably even more difficult, as they likely wouldn’t have a base of knowledge to draw upon when sifting through what’s hype vs legit in data science (spoiler: there’s a ton of data science/AI hype right now…but that’s a topic for another day).

With that in mind, below are some thoughts about the industry that some data science students might not yet appreciate:

 

It’ll probably take at least three months for you to meaningfully contribute at your first job

And in many cases, it could be way longer.

It’s hard for a newer data scientist to grasp just how critical business domain knowledge is – often as a prerequisite before you can reasonably expect to start digging through the data and producing meaningful insights.

For example, assuming you just got your first data science job in finance: if you don’t understand the basics of how the stock market works – it’ll be almost impossible for you to contribute any meaningful insights, until you have some baseline of knowledge.

Yes you’ll be able to throw out some cool graphs and maybe some spicy buzzwords – but it won’t actually be helpful to the business.

I understand the excitement, particularly with advances in deep learning, that maybe it’s becoming d less important for you to actually understand the data you’re looking at – before deploying the coolest new ML algorithm and just letting the algo start pumping out the insights.

However…I just don’t think we’re there yet.

And especially if this is your first data science job, it’s pretty unlikely that you’re skilled enough in deep learning to produce something that will rapidly and legitimately benefit the business, without first being proficient in understanding the domain.

This onboarding process of learning takes time, and I think it’s something that most companies are getting more comfortable with openly acknowledging.

For example, in discussing with my little industry group of data science buddies, the general consensus is that, on average, you’re looking at an average of six months before a new data science hire is meaningfully contributing.

 

You don’t need to already know the business domain to get hired

It’s hard enough to find quality data scientists to hire these days – and when you throw in the restriction that they also have to already be fairly knowledgeable in your domain, you could start running into major recruiting problems.

Which brings me to my next point…

 

Humility and curiosity are probably the most underrated traits in data science

It’s not really about what you currently know about the business domain – it’s more about how motivated you are to learn more about the domain.

In other words, if you’re inherently interested in learning more about the given particular industry/business area, you’re probably in pretty good shape.  Lots of would-be data scientists…just don’t really care.

For newer data scientists, this willingness and ability to learn is huge.  Technical skills are great, but in my opinion it’s way harder for a manager to find ways to get an unmotivated person to really learn about a new domain (beyond just surface-level knowledge) – vs a manager helping someone get trained in a new technical skill.

If you’re inherently curious and realistic about what you know (and don’t know), that could set you miles apart from many wannabe data scientists.

Not just in data science, but in fintech and analytics in general: right now, there are a ton of ‘experts’ walking around with high concentrations of confidence and buzzwords, but lacking the aspects of actually understanding what they’re talking about.  Even worse, many of these people have apparently no interest in learning.

Put another way: it’s less about where you are currently, and more about what’s your trajectory, what’s your progress, are you actively taking steps to learn and improve.

If you’re more interested in data science as a ‘job,’ and less interested in it as a way ‘to work on cool stuff and learn about cool things,’ then I think you’re missing out on the things that make data science most rewarding as a career.

 

Wrapping up

If you’re a student interested in data science as a career, it might be helpful to stay mindful about just how fast the industry is changing – and how learning (vs your current knowledge or skillset) is probably going to be the more strategically important long-term trait.

Put another way: to stay relevant and not become obsolete as a career data scientist, it’s probably a good idea to stay mindful of how important it is to keep adapting – even when you think you’re irreplaceable.

 

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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