The quant crunch: The demand for data science skills
In 2011, McKinsey published the report Big data: The next frontier for innovation, competition, and productivity which made significant workforce projections and said that by 2018 “140,000-190,000 more deep analytical talent positions, and 1.5 million more data-savvy managers are needed to take full advantage of big data in the United States”.
This report, along with a litany of data scientist focused articles such the Harvard Business Review article in 2012 titled Data Scientist: The Sexiest Job of the 21st Century by Thomas Davenport and DJ Patil, played key roles in driving awareness and action by educators to create new data science programs. It’s now difficult to find a major university who does not have a nascent data science program (be it a certificate, minor, undergraduate or post-graduate degree). Up-starts like Galvanize, General Assembly, and the Data Incubator began offering intensive programs in markets experiencing significant early demand for data scientists such as San Francisco, Seattle, and New York. IBM launched Data Science Fundamentals learning path on Big Data University last year.
In short, extreme focus has been placed on the nascent data scientist role but, in contrast, the much larger demand for data-savvy managers (1.5 million new positions) has largely been ignored by academia. Complementary data roles such as chief data officer, data engineer, and data governance professionals have also been largely ignored by academia and upstarts.
We divided the job role categories into 6 broad areas shown in the table below. Burning Glass then built a scoring algorithm to rank job posts as job titles vary broadly and the skills called out often are a mismatch for the title. Burning Glass also played a significant data janitor role cleansing job postings of duplicates which is a very significant challenge. There is no single repository for job postings, instead it’s more of a messy data lake. Burning Glass collects millions of online job postings from more than 40,000 sources and applies patented technology to deduplicate, mine, and code detailed data from each posting describing the specific skills, education, experience, and work activities required for the job – going well beyond the occupation and industry codes offered in other sources.
The DSA acronym used expands to Data Science & Analytics.
We had many questions, for example: what cities, what industries, which job roles were seeing strong data-savvy professional workforce growth?
Unsurprisingly, the two top hiring regions were the New York Metro and San Francisco & Silicon Valley. Rounding out the top 5 are Los Angeles, Chicago, and Washington DC. Top 3 industries are professional services, finance & insurance, and manufacturing. We had a hunch healthcare would in the top 3, but were proven wrong. Data-savvy job growth in health-care averages 6%, while the top 3 are all experiencing strong double digit growth. That said, there were some surprises. The fastest growing job skill overall in 2016, despite the industry itself seeing lower growth overall, was clinical data analyst which experienced 54% year to year growth as evidence driven decision making takes increasing hold in health care.
The top 7 fastest growing role data-savvy skills in 2016 were:
- Clinical Data Analysis: +54%
- Data Science: +40%
- Quantitative Data Analysis: +38%
- Data Visualization: +31%
- Data Engineering: +28%
- A/B Testing: +22%
- Machine Learning: +17%.
IBM projects that by 2020 the number of annual job openings for all data savvy professionals in the United States will increase by 364,000 openings to 2,720,000.