Summary:<\/em><\/strong> \u00a0The annual Burtch Works salary survey tells us a lot about which industries are using the most data scientists and the difference between higher and lower skilled data scientists.\u00a0 Salary increases show us whether demand is increasing, and finally we take a shot at determining which skills are most in demand.<\/em><\/p>\n \u00a0<\/p>\n Yes it\u2019s still one of the most interesting and rewarding career choices you can make.\u00a0 I wouldn\u2019t trade it for anything.\u00a0 Where else can you create value out of previously unvalued data while basically predicting the future?\u00a0 Of course I\u2019m talking about what customers will do, what prices or values will be, or whether something is abnormal.\u00a0 All the things we\u2019re involved with on a day-to-day basis.<\/p>\n Still, as more of us find our way into business our roles become more specialized.\u00a0 The days of data science unicorns are way behind us.\u00a0 But that\u2019s not to say that the more narrow focus we have in our new roles isn\u2019t equally creative and challenging.<\/p>\n Fortunately every year about this time we get a nice quantitative look at our career field thanks to the good folks at Burtch Works Executive Recruiting.\u00a0 For many years they have been giving us the benefit of their extensive knowledge of our job roles, skills, and salary levels thanks to their extensive relationship with several thousand data science job seekers.\u00a0 It\u2019s fascinating stuff and looking both directly at their results and reading a little between the lines we can get some really good insights.\u00a0 The data we\u2019ll share here comes from their most recent 2019 study<\/u><\/em><\/a>.<\/u><\/em><\/p>\n \u00a0<\/p>\n Are There Enough Data Scientists to Go Around?<\/strong><\/span><\/p>\n For a long time we\u2019ve been talking about a shortage of data scientists.\u00a0 That shortage looks like it\u2019s coming to an end.\u00a0 When there was a very distinct shortage just a few years ago companies had a hard time hanging on to their data scientists and annual salary increases could be in the double digits.\u00a0 That\u2019s no longer true.<\/p>\n Burtch Works reports year-over-year average salary increases for data scientists at all levels at between 0% and 4% with most groups coming in at 2%.\u00a0 This chart is for \u2018individual contributor\u2019 data scientists with one to three years experience.<\/p>\n \u00a0 A 2% average annual increase over all categories, essentially the rate of inflation, strongly signals that employers don\u2019t have to compete on wage increases to retain skilled data scientists.<\/p>\n All the same, with average individual contributor salaries ranging from $80K to $165K, and management salaries ranging from $130K to $250K depending on years experience or number of folks managed, those numbers sound plenty enticing to keep pulling people into the field.<\/p>\n \u00a0<\/p>\n Are There Too Many of Us?<\/strong><\/span><\/p>\n The way I read the data is that supply is now in equilibrium with demand.\u00a0 That is demand continues to increase as data science becomes both more widely and deeply embraced by business.\u00a0 Since that level of adoption is somewhere in the range of 25% and 50% of companies and nowhere nearly deeply entrenched enough, the field will continue to expand for many years.<\/p>\n Incidentally, it seems that there are two different forces at work here.\u00a0 The first is that we\u2019ve been graduating more and more data scientists.\u00a0 The second, which we\u2019ve been commenting on for several years, is that our AI\/ML tools now allow us to be much more efficient.\u00a0 Automated Machine Learning (AML)<\/u><\/em><\/a> or at very least much lower-code intensive platforms now allow a few data scientist to do the work of many.<\/p>\n \u00a0<\/p>\n Predictive Analytics versus Deep Learning<\/strong><\/span><\/p>\n Burtch Works makes an interesting distinction between \u2018Predictive Analytic Professionals\u2019 and \u2018Data Scientists\u2019.\u00a0 Their position is that the folks who retain them to find these folks want two distinctly different skill sets.\u00a0 To Burtch Works\u2019 way of thinking it comes down almost entirely to what we know as deep learning and the ability to write code.<\/p>\n \u00a0 Based on this distinction, Burtch Works finds that their \u2018data scientists\u2019 are the ones working with unstructured data and deep learning.\u00a0 They also see distinct educational differences with their \u2018data scientists\u2019 predominately having Ph.D.s (about 47%) while their \u2018Predictive Analytic Professionals\u2019 predominately have Masters (about 71%).<\/p>\n I\u2019ve always had a problem with this simplistic dichotomy.\u00a0 I expect you and I both know plenty of \u2018Predictive Analytic Professionals\u2019 who regularly work in unstructured data, write in Python, and are comfortable around text analytics, image processing, and computer vision.\u00a0 Not that those projects are as common as machine learning modeling and segmentation.<\/p>\n Then there\u2019s the problem that everyone wants to be a \u2018data scientist\u2019 and we\u2019ve never really worked out what a junior and senior job title should be.<\/p>\n \u00a0<\/p>\n Where are the Opportunities<\/strong><\/span><\/p>\n Setting these definitional quibbles aside, there\u2019s something interesting in the Burtch Works data about which industries are utilizing each of these categories and it\u2019s quite dramatic.<\/p>\n \u00a0 We used to associate data science with tech and Silicon Valley startups.\u00a0 It\u2019s no doubt true that innovations in the use of deep learning are still coming from there.\u00a0 But predictive analytics is now a fairly mature set of techniques and increasingly we know how to exploit it across a variety of processes and industries to create value.<\/p>\n We recently argued<\/u><\/em><\/a> that despite the hype around deep learning that most of those projects are still pretty high risk and high cost.\u00a0 These are not the sort of projects that companies starting out on their digital journey should be undertaking. \u00a0So it comes as no surprise that outside of tech\/telecom, predictive analytics is the more desired skill set.\u00a0<\/p>\n If you\u2019re doing some career planning you\u2019d like to know how many of each of these jobs there are.\u00a0 There is one hint at that in the Burtch Works data though I freely admit I\u2019m torturing the data to reach this conclusion.<\/p>\n Since Burtch Works serves a wide variety of businesses and places both \u2018data scientists\u2019 and \u2018predictive analytics professionals\u2019 we might look to the ratio of these to estimate the prevalence of each type of job.\u00a0 In their survey which comes from folks they have placed or at least tracked in their searches they analyzed 2,261 responses of which 1,840 were \u2018predictive analytic professionals\u2019 and 421 were \u2018data scientists\u2019.\u00a0 That\u2019s about 8 openings in predictive analytics for every 2 in their definition of data science.\u00a0 Intuitively that feels about right to me.<\/p>\n And for what it\u2019s worth, all you PAPs have my blessing to go right on calling yourselves data scientists while you create the lion\u2019s share of the value in your industries.<\/p>\n \u00a0<\/p>\n Other articles by Bill Vorhies<\/u><\/em><\/a><\/p>\n \u00a0<\/p>\n About the author:\u00a0 Bill is Contributing Editor for Data Science Central.\u00a0 Bill is also President & Chief Data Scientist at Data-Magnum and has practiced as a data scientist since 2001.\u00a0 His articles have been read more than 1.5 million times.<\/p>\n He can be reached at:<\/p>\n Bill@DataScienceCentral.com<\/a> or<\/span> Bill@Data-Magnum.com<\/a><\/p>\n \u00a0<\/span><\/p>\n<\/div>\n Go to Source<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<\/a>What a difference a few years can make.\u00a0 We used to say that everyone loves a data scientist \u2013 and wants to be one.\u00a0 That\u2019s still true.\u00a0 But as data science has increasingly been adopted by businesses at all levels, industries, and geographies the nature of the opportunities available to data science have also changed.<\/p>\n
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