A recent LinkedIn United States Workforce Report for January 2019 outlined continuous shortage of data scientists and data engineers across the United States. A similar trend in data talent shortage could be observed in the IBM findings which projected by 2020 the total number of jobs for all data professionals in the United States will increase by 364,000 openings to 2,720,000.
Previously LinkedIn found that demand for data scientists in the US is “off the charts”. The study concluded that, in August 2018, companies were looking for 151,717 more data scientists than US job market could offer. This number was derived by comparing the skills of over 150 million LinkedIn members with a weighted mix of skills that appear in job postings and the relative frequency at which professionals with a certain skill set are hired relative to professionals without that skill set. According to the study, the biggest data science professionals shortage is in New York City (34,032), San Francisco Bay Area (31,798), and Los Angeles (12,251). In general data tech skills like data storage, data science, and data engineering have become relatively more in demand in the largest US cities over the past three years, from 2015 to 2018.
What a time to live in. There are more data jobs available than people to fill them. A recent survey by O’Reilly indicated that the market request for data scientists and data engineers is strong not just in the United States but globally. This trend has been also confirmed by the World Economic Forum and IBM prediction model which put the yearly demand for data science, and data engineering roles to reach almost 700 thousand openings by 2020. This skills shortage affects all companies that employs a data-driven approach and can limit their abilities to deliver strong scaling strategic decisions.
Data skills, are increasingly in demand as more and more companies trying to leverage data science to become a truly data-driven organization. Below is a quick overview of top data skills for 2019:
Artificial Intelligence. To drive innovation within existing big companies and help new startups to disrupt industries and produce machines with beyond-human abilities. Autonomous vehicles, smart homes, personal assistants, security cameras, home cooking, cleaning robots, surveillance drones and robots, the list goes on and on.
Machine Learning. As a subset of artificial intelligence, it allows companies in an automated way produce models that can analyze complex data sets while delivering quicker, more accurate results.
Data Visualization. It become the de facto standard for modern business intelligence. Data visualization tools continue to play an important role in democratizing data analytics and making data-driven insights transparent to all employees.
Language processing skills are in huge demand with the rise of artificial personal assistants and a recent trend in automated customer services.
Data storage, optimization, and modeling. It has a great impact on the quality of data science projects. Where data is, how to collect and process it, and where it is best to store it, all these questions will always need an answer to facilitate data consumption for production data science development, deployment and operation.
Quantitative and Predictive Analysis, are additional very important skills for any data professional.
LinkedIn Workforce Report, United States, January 2019
LinkedIn Workforce Report, United States, August 2018
The Quant Crunch: How the Demand for Data Science Skills Is Disrupting the Job Market
How Companies Are Putting AI to Work Through Deep Learning