IBM Predicts Demand For Data Scientists Will Soar 28% By 2020
Company: IBM Česká republika, spol. s r.o.
- Jobs requiring machine learning skills are paying an average of $114,000. Advertised data scientist jobs pay an average of $105,000 and advertised data engineering jobs pay an average of $117,000.
- 59% of all Data Science and Analytics (DSA) job demand is in Finance and Insurance, Professional Services, and IT.
- Annual demand for the fast-growing new roles of data scientist, data developers, and data engineers will reach nearly 700,000 openings by 2020.
- By 2020, the number of jobs for all US data professionals will increase by 364,000 openings to 2,720,000 according to IBM.
- Data Science and Analytics (DSA) jobs remain open an average of 45 days, five days longer than the market average.
Key takeaways from the study include the following:
- 59% of all Data Science and Analytics (DSA) job demand is in Finance and Insurance, Professional Services, and IT. DSA jobs factor most prominently in the Finance and Insurance industry, where they account for 19% of all openings. The Professional Services and IT industries follow with 18% and 17% relative demand for DSA jobs, respectively. The following graphic provides an analysis of DSA job category demand by industry.
- By 2020 the number of Data Science and Analytics job listings is projected to grow by nearly 364,000 listings to approximately 2,720,000 The following summary graphic from the study highlights how in-demand data science and analytics skill sets are today and are projected to be through 2020.
- The most lucrative analytics skills include MapReduce, Apache Pig, Machine Learning, Apache Hive and Apache Hadoop. Data Science and Analytics professionals with MapReduce skills are earning $115,907 a year on average, making this the most in-demand skill according to the survey. Data science and analytics professionals with expertise in Apache Pig, Hive and Hadoop are competing for jobs paying over $100K.
- Machine learning, big data, and data science skills are the most challenging to recruit for and potentially can create the greatest disruption to ongoing product development and go-to-market strategies if not filled. The study found that the high to cost to hire, a strong need for new training programs and the high risk to future productivity of these areas is one of the greatest challenges to organizations pursuing initiatives in these areas today.
- The fastest-growing roles are Data Scientists and Advanced Analysts, which are projected to see demand spike by 28% by 2020. Data Science and Analyst jobs are among the most challenging to fill, taking five days longer to find qualified candidates than the market average. Employers are willing to pay premium salaries for professionals with expertise in these areas as well. The study found employers are willing to pay a premium of $8,736 above median bachelor’s and graduate-level salaries, with successful applicants earning a starting salary of $80,265. Experienced Data Scientists and Data Engineers are negotiating sales over $100,000.
- 39% of Data Scientists and Advanced Analyst positions require a Master’s or Ph.D. The most in-demand jobs in data science and analytics require advanced education, further driving up demand and salaries for professionals with these qualifications. The findings presented in the following table underscore just how important it is to take the initiative and create a learning program that includes formalized training when available. Continually learning and adding new knowledge in the field of data science and analytics is a great long-term personal development strategy that pays.
- It takes 53 days on average to fill an Analytics Manager position in Professional Services, making this position one most challenging to recruit for. In contrast, finding Data Science and Analytics professionals in finance can be accomplished more efficiently, as is also the case in manufacturing. The following table breaks out job categories by top industry, the average time to fill the position and average annual salary.