This post is an excerpt from our newly-released report, The Burtch Works Study: Salaries of Data Scientists 2018, which examines updated compensation and demographic data for data scientists. Download the full report for free to see how data science salaries vary by experience level, region, industry, and more, plus our insights on how these trends will shape the future of the market.
As the data science field continues to accelerate, proliferate, and evolve with changes in technology, we have several predictions on what trends will be integral to its development and expansion over the next few years.
The Push for ROI will Hound Both Legacy Organizations and Startups
Substantial investment has been poured into data science, both as traditional organizations start to collect massive amounts of unstructured data and as startups seek to use data in new ways to disrupt industries or introduce new products. In both cases, pressure has been high for data science teams to deliver returns on their significant promises.
Legacy organizations that are newly-adding data science technologies to their processes will want to see swift justifications for the investment, whereas startups that contain data science as an integral part of their DNA need to be able to show investors that they’re viable in the long-term – not just as an intriguing sales pitch.
In years past, it was usually expected that a data scientist be able to oversee every aspect of the analytical lifecycle, from data wrangling to analysis to visualization and more. Now that many teams have grown, the need for generalist “unicorns” has given way to a higher demand for data science specialists that can work with other analytics professionals and data engineers on the team. For example, we now regularly receive requests for experts in specific areas such as NLP (natural language processing) or image processing with tools like TensorFlow.
That being said, generalists may still be in demand for firms hiring their first data scientist, or for those that have many, varied use cases for data science and therefore need a wide range of skills. Hiring “unicorns” with broad skillsets used to be the norm in data science, but now generalists are likely to be preferred only in specific situations.
For the most part, as many firms have further developed their specific needs, hiring managers are searching for more specific skills in areas of data science. Additionally, as the supply of data scientists and their education options increase, we’re beginning to see professionals segmenting into specialized areas of data science because they have more opportunities to do so.
Burtch Works has always advocated for continuous learning to be at the center of a long-term career strategy, but, now more than ever, staying hands-on has become a key component, even for senior leadership positions.
In a field that evolves so quickly and where leaders are often expected to exhibit a “player coach” mentality, remaining hands-on with the data is the best way to stay current with new tools and technologies. In cases where a role doesn’t necessarily accommodate this strategy, some senior-level data science managers choose to spend some of their personal time taking part in quantitative competitions to stay sharp. Data scientists who find themselves straying too far away from technical work may quickly find their skills out of date and unmarketable in today’s climate.
In closing, for the past several years we’ve pointed to several growing trends that show how data science has begun to spread beyond its original borders. Predictive analytics professionals that have typically work with structured data have begun to transition their skills into data science roles, data science is no longer limited to giant tech firms and startups on the coasts, and many industries outside the typical “digital native” profile are adding data science technologies to their repertoire.
These changes have not only had a flattening effect on some of the salary disparities we’ve seen in years past, but it has also led to increased opportunities, both for data scientists looking to live somewhere besides Silicon Valley and for firms outside the West Coast who have struggled to lure data scientists out of their coastal geographies. We expect to see more industry shifts as the use of data science continues to spread and mature, and look forward to sharing them in future reports.