2018 Predictions for the Analytics & Data Science Hiring Market
Linda Burtch, Managing Director at Burtch Works | 30+ years’ experience in quantitative recruiting
Several years into the “Big Data” hype, analytics and data science professionals across the country are facing a changed landscape and wondering “what’s next?”Our annual predictions have always been a popular topic, so this year I dug deep into the data and insights we’ve been gathering to see if I can divine where we’re headed. As always, your own thoughts and predictions are welcome in the comments too!
1. Data Scientist title continues to reign on
Despite conflicting definitions for the term, “Data Scientist” continues to be the moniker of choice for employers looking to gain visibility for their jobs, whether the role is data science or not. Since this trend is likely to continue into the foreseeable future, we recommend reading up on how you can read between the lines when evaluating data science or analytics job descriptions.
2. Sophisticated analytics becomes more accessible
As tools continue to evolve, data science techniques are maturing and becoming more mainstream. While not everyone will be an analytics whiz or a data scientist, the techniques are going to become more accessible and available to professionals who may not be as technical.
3. Even your grandparents will know what artificial intelligence is
Although they may not be able to clearly explain what it is, AI chatter has become widespread enough that even the more non-technical people you know will probably have heard of it. With buzz about data-driven decision-making becoming inescapable, ads from companies like GE and IBM are introducing high tech concepts to more people than ever.
4. Companies will evaluate their return on investment from analytics
Over five years after Data Scientist was declared the 21st century’s “sexiest job” and with substantial investment poured into data science and analytics teams, companies are wondering what they’re really getting for their money. Expect more shakeups if teams can’t show their ROI and prove their worth.
5. Python is the new R
Last year’s SAS, R, or Python survey results showed that Python is gaining in popularity among both data scientists and traditional analytics professionals. As a stable and scalable option with tons of functionality, Python is quickly becoming the tool of choice over R. And, as an open source tool, Python’s versatility is continuing to develop at the field matures.
6. Employers favoring other tools will hesitate to hire SAS-only professionals
Once the gold standard in analytics tools, SAS is now more a marker of an outdated skillset. Analytics professionals who are only proficient in SAS will find themselves limited to companies who are still lagging behind the open source movement. Knowing only one language is quickly falling out of fashion, so make sure you update your skills before you get left behind! In a field that changes so fast, you should never get too comfortable. Python may be the hot tool today, but it could be something else tomorrow.
7. Staying hands-on is more important than ever
While we’ve always recommended continuous learning as a way to stay current, it’s become clear that staying hands-on with data projects is key, even for top-level management positions. This is especially common in tech or younger firms, which have a much flatter organizational structure than traditional companies. In a market where your skills can become obsolete in just a few years, staying close to the data is the best way to make sure you keep your skills fresh and marketable.
8. China is the Wild West of analytics
China is accelerating quickly as a quantitative powerhouse, with not only investments in US companies, but also a booming local tech scene with companies like Baidu, Alibaba, and Tencent. With access to troves of data and a developing economy, be on the lookout for Chinese firms snatching up talent from the US.
9. Traditional organizations are spawning quantitative startups
Over the past few years we’ve noticed more traditional companies have been spawning new ventures to develop a new idea or product outside of the larger organization, and many of them have data in their DNA. These faster, more agile startups include companies like Arity, which was created by Allstate, and Gamut, created by Grainger. These groups have the advantage of being backed a large corporation, while still being able to think and react quickly. Creating some distance between the parent company and the startup entity can also help with recruiting young talent that might hesitate to join a traditional name-brand organization.
10. The acquihire is big in Data Science
With the continued lack of talent, companies looking to hire large numbers of data scientists are having to get creative about where they find quantitative experts. Popular among Silicon Valley tech giants like Google and Facebook over the past few years, the acquihire is starting to see some popularity outside the Valley as well, as I’m seeing companies snapping up data science consulting firms in a bid to absorb their talent.
What won’t happen:
1. Data scientists will not become obsolete through automation
While I think it’s fair to expect that many “data janitorial” tasks will be automated in the future, I believe this will free up data scientists for more higher-level tasks, as opposed to automating them out of existence. Automation may shift job responsibilities for data scientists, but analytical thinking and the ability to communicate strategic insights from data won’t go out of style any time soon. And, we are not yet close to filling all the job openings for quantitative professionals.What do you think of this year’s predictions? Do you see any new tools on the horizon, or do you believe data science popularity is due for a reckoning of sorts? Let me know in the comments!
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