From Graduate to Data Scientist: Can You? Can I?
It’s taken me 18 months since graduating to decide what I want to do first. I’ve tried a couple of things; designing solar farms, selling experiences in the mountains, taking people scuba diving and being a generalist at multiple Tech start-ups. The journey has been long but discovering Data Science has very much excited me. This mountain of data that we are producing can be and is being used. And it is being used well.
In the noise produced from all of this data there is a signal. Stitch Fix are finding it and by using it they’re making sure you’re wearing the right clothes and feeling confident. Transport for London are learning how to optimise your commute at the moment. Imagine that, a better commute?
Mind, this has been happening for a while. Your loyalty card from Nectar or your John Lewis Credit Card has allowed you to receive coupons in the post every couple of months. These aren’t random, there’s some serious science in play. Each coupon is connected to your spending habits, making sure they’re useful for you. To achieve this they’ve employed Data Science and they’ve employed it to great success.
So, How do I become a Data Scientist?
As I’m learning at the moment, it’s difficult. As more and more data is being produced and stored by companies all around us we’re only just starting to realise the value of this resource. And much like oil, gas and other resources data is another resource for which we’re finally building an engine to use it.
Companies know this and they’re hiring, a lot (according to Hired). Data Scientists are wanted and needed everywhere. But there’s a catch…
Enter the CEO’s internal dialogue.
“Ok, I want a Data Scientist.”
“We’ve got all of this data that we’re storing. Apparently there’s value in it, and I believe it.”
“Fine. Let’s say there is. Who’s going to play the prospector?”
“That’s what a Data Scientist is for?”
“What are they going to do?”
“We’ll hand them our dataset. They’ll optimise the business and play oracle. Through them we’ll always know what to do.”
“Sounds great! Let’s get a Data Scientist then”
What’s the catch? Experience. The Data Scientist that they want is someone who will know what to do. They can simply give them data and the capital to build a team and in 6 months to 2 years they’ll produce a profit.
This shows the first problem with the overall market for Data Scientists at the moment. Data Scientists are being employed without direction. A CEO/CTO will build a data science team without an idea of what they can do and just expect results. This particular C-level lacks the experience to get the most value out of a Data Scientist. The solution? There needs to be an intermediary, someone who will give direction, the correct direction too. That someone will have experience.
Back into the mind of the employer.
“Ok. So I want a Data Scientist with experience. I also need to have an idea of what I want the Data Scientist to do.”
Calls the CTO and builds a strategy for the data. (Easier said than done, probably involving more people too…)
Once complete, they call in the Head of Talent Acquisition.
To the Head of TA, “We need a Data Science team. We need about 5 people with 5+ years experience.”
Head of TA: “OK”, whilst thinking internally “Doesn’t ask much…”
The second problem now rears it’s head. Supply and demand. Demand outstrips supply, a lot. Experienced data scientists are rare and when found they aren’t cheap.
“So how does this apply to me, the graduate?” you ask the gods of capitalism. They reply a simple “Good Luck.”.
A healthy graduate market with a stable recruitment funnel (what you’ll experience with finance and consulting) cannot exist without other, more experienced people offering guidance and training.
So, if you, like me want to be a Data Scientist there’s an uphill struggle ahead. There’s no obvious path (without increasing your debt). Companies such as Accenture and Deloitte pay a lot of money to make their hill is more visible than others so you know that they’re an option when you leave university.
The majority of companies don’t yet need graduate data scientists. They need experienced data scientists. The only graduates I’ve seen working in such a capacity have been working for Channel 4 (and they’re doing some cool stuff).
But this doesn’t mean that you should give up on the idea. In the graduate market as a whole the supply outstrips demand, especially for great jobs. If you want to stand out in both the graduate employment market and the general employment market you need to shine and figuring out a way to become one of the first graduate data scientists is a way of doing that. You’ll honestly be able to call yourself a pioneer.
Mind, if you’ve got an applicable PhD you’ll find it easier. Pivigo’s S2DS is an obvious starting point.
If you want to know how I’m planning on becoming a Data Scientist watch this space…