ACM CareerNews for Tuesday, June 21, 2022
ACM CareerNews is intended as an objective career news digest for busy IT professionals. Views expressed are not necessarily those of ACM. To send comments, please write to firstname.lastname@example.org
Volume 18, Issue 12, June 21, 2022
Tech hiring continues at a rapid pace in the U.S., with software developers and engineers now accounting for approximately one-third of all job postings. Based on data from the U.S. Bureau of Labor Statistics, employer job postings for tech roles reached a record high in May 2022, led by new hiring in IT services and software development. Across all markets, job postings for tech positions totaled 623,627 in May and nearly 2.2 million year-to-date. This represents a 52% increase compared to the same period of the previous year. Tech sector companies added 22,800 new workers in May, while the total number of job advertisements for software developers and engineers hit 204,084, an increase of more than 77,000 from April.
As might be expected, hiring activity was focused around major tech hubs and metro areas. New York City, Dallas, Los Angeles and Washington, for example, all recorded tech job-posting totals that surpassed 31,000 positions. Tech jobs advertised as remote positions also continue to grow in the U.S., which is contrary to other developed markets, where recent trends have indicated a fall in the number of remote-working roles. According to estimates, a total of 106,386 tech jobs offering remote working and work-from-home were added in May, led by software development and engineering, web development, network engineering, and IT project management. The data speaks to the broad-based nature of the tech workforce, and it also speaks to the many factors affecting employment and situations where sectors or companies easing up on hiring may be offset by sectors or companies increasing hiring.
Recent growth in the healthcare IT industry has created more jobs, demand for a broader spectrum of skills, and more places to work. Roles in this field range from user experience designers to health informatics specialists at medical centers, physician practices, startup healthcare ventures, and vendor companies. Overall, there has been tremendous growth in healthcare IT, with work moving beyond supporting legacy systems and just keeping systems up and running. There has also been a lot of innovation, and as a result, there are more opportunities than ever before for tech professionals looking to enter or advance their careers by moving into healthcare IT.
The healthcare IT industry is experiencing unprecedented demand when it comes to technology workers. While job statistics focused specifically on the full range of positions that comprise healthcare IT are not readily available, figures from the U.S. Bureau of Labor Statistics (BLS) provide some perspective. The BLS, which groups medical records and health information specialists together, puts job growth in this sector at 9% from 2020 to 2030. It predicts an average of 34,300 job openings annually for the decade, much of it due to workers leaving the field as they retire or transfer into other areas. The industry could add 37,100 new positions over the decade, for a total of 453,500 by 2030. According to some estimates, data engineer positions in this industry grew 122% from 2019 to 2022, data scientist positions grew 108% in those three years, web designer jobs grew 107%, mobile app developer jobs grew 73%, and user interface and user experience positions grew 52%. All of those figures are higher than the growth seen in those positions in other industries.
Rapid changes in fields such as artificial intelligence (AI), robotics, and automation mean that the future of work may look very different than it does today. In fact, experts have predicted that 85% of the jobs that will be available in 2030 do not yet exist. Factors such as the widespread shift to remote work, the emergence of the gig economy, and increasing expectations of flexibility in employee relationships with their employers will play a big role. This rapid period of new job creation may sound exciting, but individuals are finding themselves increasingly confused about what is expected of them and what types of skills and experience will be valued in their own search for secure and meaningful careers.
Running a business, managing a busy department, or dealing with complex employee problems, involves taking a far higher-level and more strategic overview of the goals, options, and resources involved. The ability to construct solutions requires imagination and creative thinking far beyond what even the most complex neural network is capable of. This means that some of the most in-demand skills of the workplace of the near future will revolve around tackling high-level challenges through leadership, human judgment, complex decision-making, collaboration and team-working, digital threat awareness, awareness of issues of ethics, culture, and diversity. Understanding how to harness these human-centric skills will put you in a position to be able to work effectively alongside whatever technology comes along to disrupt your industry without having to worry that it is going to replace you.
To Solve Your Data Science Talent Gap Embrace Diversity
Information Week, June 13
In order to fill all of their open data science roles, organizations need to acknowledge that data scientists come from all walks of life with a variety of skills, experience, and training. In large part, this willingness to embrace diversity in the workforce is the result of a growing talent gap. Their backgrounds range from computer science and applied physics to bioinformatics and beyond. Some come from non-quantitative backgrounds, while others have become data scientists through online training programs or coding bootcamps. Job postings for data scientists have grown at a rapid rate in recent years, and nearly every fast-growing organization needs more data scientists. They are the crucial ingredient for turning raw data into innovative new products and services, and data-driven business transformation.
Every organization that wants to scale its data science capabilities needs to embrace the current diversity of data scientist backgrounds and profiles. However, most organizations persistently work against their own interests. They attempt to over-standardize data science job descriptions and career paths and seek to limit data scientists to a narrow set of tools. All these actions limit the ability of an organization to scale. Instead, organizations need to plan for and establish a workplace that is inclusive of all data scientists. To do so, they must tackle the people, process, and technology aspects of data science diversity. Recruiters and hiring managers need to explicitly seek candidates from a range of different academic backgrounds, experiences, and skills.
5 Job Interview Questions to Expect During the Great Resignation
Dice Insights, June 14
In order to put your best foot forward during your next job interview, there are some important questions you should anticipate and be prepared to answer. First and foremost, realize that many employers are attempting to see if you will be a good fit for the job, and their questions will reflect this. As part of their evaluation, employers might probe your motivation, commitment, career objectives, work preferences and overall job expectations. Since one of the primary causes of employee turnover is poor cultural fit, interviewers will often ask you to describe the type of culture and work environment that motivates you, as well as the management style you prefer.
While many tech workers are looking for flexibility, remember that flexibility is a two-way street. For example, while you might want to work remotely full-time, with a completely flexible schedule, you might consider meeting the company halfway by agreeing to hybrid work and a pre-set schedule. By signaling your willingness to help the company meet its objectives and cultural needs, you improve your chances of landing the job. If you have a track record of leaving jobs within a year, numerous employment gaps on your resume, or quit your last job without another lined up, expect the interviewer to dig deep to see if you lack commitment. Provide a brief, acceptable reason for leaving, such as wanting more upward mobility, a more inclusive culture, or the chance to work on projects using innovative technology. Then pivot to what you are looking for in your new role.
Coding Leaders Forge a New Path For IT Career Advancement
CIO.com, May 27
For software developers thinking about a future career in management, the relatively new position of coding leader represents a potential opportunity. The new job title comes in response to a common dilemma: Many developers want to expand into management as their mastery of technology gives them the confidence, but they do not want to abandon the practice that brings them fulfillment. In short, the coding leader is a new kind of leader responsible for both strategy and being hands-on with technology. This leader is able to walk in the worlds of business and technology with equal aptitude. By staying up to date with the practice of coding, these leaders maintain insight into the workings of the projects, stay on top of industry developments, and can perceive where changes can best benefit the organization.
The popularity of the coding leader position is proof that programmers can be good leaders. As developers grow in their role, their vision encompasses more of the systems and processes at play, with understanding of the individual elements. As a skilled developer becomes really experienced, especially when their knowledge of the specific system under development becomes expansive, they are able to dip into high-value areas, assist with making changes, and maintain the high-level view. Adding to this an appreciation for the business side of things makes for a potent combination of talents. The mindset change that is required of coders here is to allow for a true balancing of priorities. While working developers may tend to see anything but actual coding as simply an interruption, successful coding leaders can hold the importance of both business and technical needs in mind. The coding leader knows how to keep a broad perspective that incorporates both the trees and the forest, how to shift between them, and, especially, how to allow the two to inform each other so insight flows between them.
How to Live With Job Title Inflation
Protocol, June 16
In a talent market where every bargaining chip matters, companies are losing both candidates and current employees to competitors willing to get creative with job titles. In the tech industry, especially, job title inflation is out of control. Promoting people and giving them almost double-promotions, without the experience, is an increasingly common phenomenon. Seemingly almost overnight, relatively young high performers are being given titles such as VP that used to take a decade or more to earn. For obvious reasons, these new job titles are highly effective as recruiting tools. Until hiring across the industry slows, though, organizations will need to find a way to live with job title inflation. Otherwise, they risk losing employees who are eager to jump ahead in the ranks without putting in the necessary work.
Employers and recruiters have a clear incentive to make job titles sound more attractive. When unable to offer a competitive compensation package, a loftier title can go a long way. Recruiters have found that many high-achievers are emotional and ego-driven, so if it is a title that seals that deal, oftentimes organizations are happy to accommodate that. However, inflated job titles may solicit unqualified candidates. Moreover, excessive title inflation can hurt the reputations of companies and recruiters, and could even call into question how qualified existing teams are. At the end of the day, though, some title inflation is just good marketing. If you are hiring people, though, you really need to dig into what a specific title means on their resume, rather than just assuming it is a meaningful role.
IT Talent Shortage Challenges and How to Solve Them
The Enterprisers Project, June 15
When traditional strategies fail to fill in-demand tech jobs, organizations need to consider a variety of non-traditional means to find the best candidates. In the current hiring environment, in which talented job candidates still hold the upper hand, frequent recruiter calls and conventional job board postings might not be enough. Simply put, hiring managers might be searching for talent in the wrong places or using ineffective methods to find and then hire the best talent. With that in mind, the article provides an overview of several strategies that can result in hiring success in competitive situations.
When conventional job boards fail to produce candidates, it might be time to look elsewhere. Instead of large job boards, it might be time to consider industry-specific or highly-specialized job boards. For example, AngelList deserves its high reputation in the tech world, and many startups post roles there. Yet, LinkedIn, another tried-and-true avenue, yields better and swifter results when hunting for more unusual skills. While the relatively small size of the data science community poses challenges to businesses looking to hire, data people are a genuine community, and actively participating in that community can be a powerful tool. When engineers contribute to open source projects, an organization should be prompt to highlight that work in relevant communities.
The Profession of IT: Involvement and Detachment
Communications of the ACM, June 2022
When thinking about your future career trajectory and how much satisfaction you take away from your job, it is worth considering how you relate to others within your company, your industry and society as a whole. Do you tend to define problems, issues and phenomena in terms of data, statistics and properties? Or do you prefer to get more involved in learning about the people, organizations, and communities behind those problems or issues? Broadly speaking, this can be defined as the difference between detachment and involvement. Detachment orients us to be an outside observer of our community, while involvement orients us to establish new connections with fellow human beings.
The current era seems to value detachment over involvement. Abstraction is a popular tool to transform seemingly impossible problems into possible solutions. In order to do so, we must use statistics, data and scientific observations in order to characterize large populations and large-scale phenomena. This is natural for both corporations and governments, which are preoccupied with defining and dispensing services efficiently across large populations. Global connectivity enables data collection from everyone and distillation of trends in large groups, revealing large-scale phenomena that were not visible in prior times. The ability to view large-scale phenomena through the lens of distilled data is a strong force for abstraction. Unfortunately, abstraction is also a force for detachment, the loss of connection with fellow human beings.
AI as Natural Science?
Blog@CACM, June 8
Since its inception, AI has existed in a gray area between engineering, which aims at designing systems for specific functions, and science, which aims to discover the regularities in naturally occurring phenomena. The science part of AI came from its original attempts to provide insights into the nature of human intelligence, while the engineering part came from a focus on getting computers to demonstrate intelligent behavior. This situation is changing rapidly, especially as AI is becoming synonymous with large learned models. Some of these systems are coming to a point where we not only do not know how the models we trained are able to show specific capabilities, we are very much in the dark even about what capabilities they might have. Often, even their creators are caught off guard by things these systems seem capable of doing or new emergent behaviors.
Given the fact that it is getting harder and harder to predict the emergent behaviors of learned AI systems, it is clear that at least part of AI is straying firmly away from its engineering roots. It is increasingly hard to consider large learned systems as designed in the traditional sense of the word, with a specific purpose in mind. After all, engineering disciplines do not typically spend their time celebrating emergent properties of their designed artifacts. Increasingly, the study of these large trained systems seems destined to become a kind of natural science, even if an ersatz one: observing the capabilities they seem to have, doing a few studies here and there, and trying to develop at least a qualitative understanding of the best practices for getting good performance out of them. Indeed, machine learning is replete with research efforts focused more on why the system is doing what it is doing, instead of proving that we designed the system to do so. Of course, there might be significant methodological resistance and reservations to this shift.
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