How to Solve the Talent Shortage in Data Science
Recent years have brought an explosion of data science jobs. Data analysts, data miners, big data scientists, and more similar job titles populate online job boards now. This is a new domain, and very few people, if any, occupying these positions have an academic qualification in this exact field.
Most data scientists come from other jobs or entirely different career paths. Some were engineers; others were programmers, statisticians or mathematicians. Almost any job that requires a solid understanding of logic and statistics can act as a launching point for a career in data science. Here is an outlook on a few trends identified, proposed and analyzed Call for specialists and fierce competition
As most companies are shifting toward a data-driven approach, there is a higher demand for data scientists, either for in-house development or for software providers offering SaaS products. There is a definite scarcity of these specialists; they need to have a rare combination of skills and a multidisciplinary thinking, which has not been encouraged The entrepreneurial mindset in a corporate environment
Another reason why candidates for data science jobs are hard to find in a typical corporate environment is that this multidisciplinary approach is more characteristic of entrepreneurs. They need to connect the dots, come up with ideas that have not been tested before, and innovate.
Prior experience in number-dominated fields helps but is not enough. Also, the best results are achieved Migration from academia
The set of analytical skills required More job titles
The world of job titles is confusing right now because many organizations tend to use titles as a way of employee gratification. It is not unusual to see employees in mid-level positions called VP just to give them an ego boost instead of a financial one.
In the world of data science, it gets more confusing due to the novelty of the job requirements. It is not uncommon to call any of the following a data scientist: those who perform SQL queries, data cleaners, machine learning roles (from architects to testers) and researchers. As you can imagine, each of these is in fact a separate job. However, until now these roles were not distinctive enough to get a title on their own.
As data science leaps into the future, there will be less demarcation between data scientists per se and other roles, such as product managers. This already happens in companies, like Quora, that are at the forefront of online innovation when it comes to data applications.
No talk about the future of data science jobs is complete without mentioning the impact on other jobs. As automation’s scale and pace increase, data scientists are likely to put others out of their jobs. Although there will be no shortage of work in the future, there will be some imbalance between highly skilled positions and the low-end of the working spectrum.
Emilia Marius is a senior business analyst and project manager software development company Boldare. Combining 8-plus years of expertise in delivering data analytics solutions with 3-plus years in project management, she has been leading both business intelligence and big data projects, as well as helping companies embrace the advantages that data science and machine learning can bring.