According to a recent DevSkiller report, data science was the fastest growing IT skills in 2021, with a 295 percent increase in the number of data science-related tasks recruiters set for candidates during the interview process last year. As this hot market only gets hotter, read on to learn more on five emerging trends in the data science job market and their potential impact on hiring managers, companies, and candidates alike.
Upskilling and Education a Chief Priority
As mentioned above, there has been a surge in demand for data science skills, so it should come as no surprise that candidates are increasingly interested in how companies can provide the educational opportunities necessary for meeting this demand. In particular, recent graduates and less experienced data professionals are looking to organizations to facilitate the learning, upskilling, or mentorship they need to take their skills to the next level. In light of this, companies looking to attract and retain top data talent would be wise to build connections in the education sector to help stand out in the competitive job market.
Data Specialists Preferred to Data Generalists
Organizations are looking for data science specialists who can apply their skills out of the gate to a specific business problem. Niche skill sets such as deep learning engineers, risk data scientists, machine learning engineers, data engineers, and computer vision engineers are becoming more sought out. Candidates with more generalized data science experience should invest in training to build their expertise in these specialized areas.
Interest in a Strong Company Mission
Recognizing that they wield much of the power in today’s competitive job market, many data science candidates are waiting to accept a position until they find a company whose mission aligns with their personal interests and values. Companies should respond to this by incorporating their values into the recruitment process so candidates have a clear understanding of what the organization stands for before, during, and after the interview.
Disorganized Data Goals
It’s not uncommon for businesses to hire data science talent before realizing the exact scope of their projects. These disorganized data goals can easily lead to frustration for the newly hired data scientists. Given the wealth of available jobs, these employees can, and will, quickly move on to new roles.
Preparing Teams for Digital Transformation
Many companies are rounding out their data science teams as they plan for the next phase of their digital transformation journey. In the coming year, expect to see a greater hiring push around how various analytics roles can be applied to digital transformation initiatives.
For more on these five trends and how candidates and companies can best prepare for them, check out this Datamation article.