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4 Major Trends Disrupting the Data Science Market

June 27, 2016
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This blog post is an excerpt from our recently published Burtch Works Study: Salaries of Data Scientists 2016, which has updated compensation and demographic data for data scientists across the US. The full report can be downloaded for free here.The evolution of data science, the maturation of data scientists, and the disruption taking place in many of the industries in which they work all raise the question – where do we go from here? From our vantage point at the intersection of data scientists’ perspectives and organizations’ hiring needs, there are several trends that we foresee taking shape over the next few years.

  1. Data scientists shifting towards mission-driven goals

One interesting trend is the shift, which has begun to take place in our conversations with many data scientists, from a desire to work for bigger, “name brand” companies like Facebook or Google, to more mission-driven organizations attempting to make an impact on society. Although the massive data science teams at FANG (Facebook, Amazon, Netflix, and Google) and others continue to attract top-notch talent, there has been a noticeable segment of data scientists leaving or avoiding companies like this in order to build something new, or in hopes of contributing to something that they feel is more meaningful. Whether it is curing cancer, conserving energy, tracking infectious diseases, or personalizing education, more data scientists are becoming interested in trying to make the world a better place. An initiative like DataKind, where data scientists can volunteer their talents for social good, is just one example of this.

  1. Increasing pressure on unicorn startups

Another trend that has started to coalesce over the past few months, and which we anticipate will continue to take shape, has been the increasing pressure on many startups to show their value. Over the past three to four years, many data scientists have chosen to work for startups, but as some have already been through the startup cycle once (or even twice), many are now looking for something with more stability. If a startup struggles to meet investors’ expectations – either because it’s not profitable, can’t scale, or both – this can spell trouble for some teams, and in turn for the data scientists they employ. Lately there have been whisperings of overvaluation, since the number of unicorn startups valued over $1 billion has increased from 45 to 149 companies worldwide (as of June 2016) over the past two years. If there are storm clouds on the horizon, it will have a noticeable effect on the data science hiring market.

  1. Applications for data science cropping up in new industries

In addition to industries where the use of data science is more established, there are other industries like fintech, healthcare, and transportation where data science as a discipline is young and growing. Companies in consumer lending are refining how to assess creditworthiness using non-traditional factors like social media networks. Healthcare startups are using data science to move ever-closer to personalized medicine and using artificial intelligence to examine images like x-rays and MRIs to diagnose problems quickly and accurately. In transportation, traffic management agencies are able to use real-time traffic and weather data to predict traffic flows and manage emergency response. As the utilization of data science in these industries matures, no doubt we will see even more new applications for it.

  1. Hot hiring market diversifying educational backgrounds

An issue that has continued to plague companies in every industry has been the dearth of experienced data science talent available to adequately embark on all of these new projects. Whether in advertising, life sciences, government, cybersecurity, insurance, technology, or finance, all have struggled to find talent beyond the entry level. Demand for data scientists has been increasing as more organizations jump on board the “data bandwagon”, and while the supply has been improving, it still lags far behind. Luckily, more educational options – including new Master’s degrees, MOOCs, and bootcamps – have sprung up to try and meet this need. We’re already seeing an increase in the percentage of professionals with Master’s degrees (as opposed to Ph.D.’s) at the junior level, and educational backgrounds will continue to diversify as more students and professionals look to capitalize on the hot market. Some companies, like Airbnb, are even hosting internal bootcamps, opting to “make rather than buy”.As one might imagine, all of these factors combined will lead to a lot of changes over the next few years. The use of data science will become more ubiquitous, the talent supply will improve, and there will be even more use cases for these techniques, reaching far beyond the few examples of current uses that we’ve briefly mentioned. Consider the report a glimpse into the future of where the discipline might be headed, and no doubt next year we’ll have even more news from the front.

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