Earlier this year, Burtch Works’ Managing Director and expert in quantitative recruiting, Linda Burtch, released her annual predictions for the data science & analytics hiring market. Each year she has also hosted an accompanying webinar to delve deeper into the trends she highlights (you can watch the recording here) with a live Q&A at the end.
Since we know not everyone has time to watch a video, we thought we’d share a transcript of the questions Linda answered about 2020 trends (and beyond) so that our readers could check out her insights on some of these topics!
So first question for Linda, are there particular areas and data science with high demand such as deep learning or image recognition.
Linda Burtch: That’s a good question. Certainly there are lots and lots of applications in deep learning that are really growing. However, you have to keep in mind that the vast majority of the applications are still in what I describe as Predictive Analytics. So they’re working with data that’s fairly structured. The deep learning side is growing pretty rapidly so that’s something that we keep an eye on pretty closely here.
We’re also seeing some blending which I think is interesting. What I mean by that is that a lot of people who have a more traditional Predictive Analytics background are getting into working with unstructured data, and the algorithms that are available now are much more sophisticated than they were even three or four years ago.
The next question is about your prediction about the shakeout in startups and to talk a little bit more about what you mean about that.
Of course. So there’s a lot of hype going on with AI application and monetizing data and so on and so forth, and there’s a lot of VC money out there in California and it’s sort of spreading to other parts of the country. New York certainly and even throughout the Midwest, and there are people going for the moonshot opportunities – the next Facebook kind of thing – but there’s only so many of those applications that I think are going to actually make it.
So there’ll be a lot of fallout, but you know, there’ll be some winners too. So it’ll be really interesting to watch. You know, it sort of reminds me of what things were like back in 1999 and to early 2000 with the e-commerce bust. Anybody who was around then may remember the fallout that happened during those 12 months or so spanning 99 and 2000 to the e-commerce platforms that were sprouting up everywhere.
The next question is about the data translator role that we’ve talked about in the past and conducted a flash survey about and asking what, if any, predictions do you have for that role and what kind of background do you see that kind of person having?
Yeah, the data translator are the citizen data scientists. Is it sometimes called a Program Manager. Even then, it’s important to have a solid quantitative foundation. Although you don’t necessarily need to have somebody who’s going to be able to develop complex code or algorithms, they need to be able to communicate and evangelize the use of particular tools and embed those into the business units.
So it’s a really critically important role within the organization, but it’s very new though. It’s only been around for maybe two or three years, so it’s hard to for me to say that I’ve seen any patterns. I’ve seen people go from analytics into this type of role, but I’ve also seen people in the business units put up their hand and say, you know, I want to be this person. What remains to be seen then is where do you go from that role? And I don’t know what the answer to that is. I’d be a little concerned about that if I were a quantitative person going into that role, because once you transition away from working hands-on with the data it can be hard to go back.
We have a couple people that are asking about learning Python and if we have any suggestions on the best way to go about adding that skill to their resume.
There’s lots and lots of resources available online. So definitely do your research, but the best way to learn Python is to use it, so find an application and use it. I mean everybody out there knows this, but it bears repeating that you can’t learn to program from a book. It’s very important to get data, jump in on it, and learn.
Alright and then the last question I think we’ll do, and I feel like we get this one every year, is are there any industries that you see emerging as far as data science and analytics?
Okay, I’m thinking… boy, I can’t say that I’ve seen any that aren’t emerging! I mean there’s just so many. Certainly Retail, Financial Services, you know, those are a couple of areas where data and analytics have been around for many years. And Healthcare, there’s some really, really interesting things going on in Healthcare. Government needs to step up to the plate and get more sophisticated here.
One interesting recent example is, I’m talking to a cement company and you know, I’m thinking, “Okay, how can analytics be used in a cement company?” But the number one goal of this analytics team is to reduce their carbon footprint by smarter use of data and building efficiencies into their process, which I think is just unbelievable and I had never thought of it. Energy is another area too. So applications are really coming up everywhere, and it’s very, very exciting.
Transcript has been lightly edited for length and clarity.
Want to learn more about Linda’s predictions and their implications for quantitative careers and employers? You can view the 30-minute webinar recording below, where she discusses these trends in more detail.